MSDS 6306: Doing Data Science
Kris Ghimire
3/24/2020
R Markdown
DDSAnalytics Talent Management Solution:
Introduction:
Keeping employees happy in the company and hiring best match for the specific role is one of the most important aspect of Talent management. People are the backbone of any company. All the resources, infrastructures, and technology provide no value to the company without the topmost talented and skilled employees. Hence, the biggest challenges that talent management company faces every day is not being able to have enough data on attracting and retaining the talented employees. Therefore, our goal in this project is to leverage the power of data science to conduct an analysis on existing employee data to provide some interesting trends that may exists in data set, identify top factors that contribute to turnover and build a model to classify attrition and predict monthly income.
# Question to Address in Analysis:
# Q.1 Identify factors that lead to attrition backed up by evidence provided by analysis.
#Q. 2. Find some specific trends that may exist in the data set (e.g., “Data Scientists have the highest job satisfaction”).
#Q.3. Also provide any other interesting trends and observations from your analysis.#libraries
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## %+%, alphaDDSAnalytics <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)
head(DDSAnalytics)## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 13 4 Life Sciences 1 859
## 2 14 3 Medical 1 1128
## 3 18 2 Life Sciences 1 1412
## 4 1 4 Marketing 1 2016
## 5 2 1 Technical Degree 1 1646
## 6 10 2 Life Sciences 1 733
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 2
## 2 3 Male 44 2 5
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 1
## 6 4 Male 32 3 3
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## 6 Manufacturing Director 1 Divorced 8793
## MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## 1 9250 2 Y No 11
## 2 17544 1 Y No 14
## 3 19944 2 Y No 11
## 4 24032 1 Y No 19
## 5 17218 1 Y Yes 13
## 6 4809 1 Y No 21
## PerformanceRating RelationshipSatisfaction StandardHours
## 1 3 3 80
## 2 3 1 80
## 3 3 3 80
## 4 3 3 80
## 5 3 3 80
## 6 4 3 80
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## 1 1 8 3 2
## 2 0 21 2 4
## 3 0 10 2 3
## 4 2 14 3 3
## 5 0 6 2 3
## 6 2 9 4 2
## YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
## 1 5 2 0
## 2 20 7 4
## 3 2 2 2
## 4 14 10 5
## 5 6 3 1
## 6 9 7 1
## YearsWithCurrManager
## 1 3
## 2 9
## 3 2
## 4 7
## 5 3
## 6 7summary(DDSAnalytics)## ID Age Attrition BusinessTravel
## Min. : 1.0 Min. :18.00 No :730 Non-Travel : 94
## 1st Qu.:218.2 1st Qu.:30.00 Yes:140 Travel_Frequently:158
## Median :435.5 Median :35.00 Travel_Rarely :618
## Mean :435.5 Mean :36.83
## 3rd Qu.:652.8 3rd Qu.:43.00
## Max. :870.0 Max. :60.00
##
## DailyRate Department DistanceFromHome
## Min. : 103.0 Human Resources : 35 Min. : 1.000
## 1st Qu.: 472.5 Research & Development:562 1st Qu.: 2.000
## Median : 817.5 Sales :273 Median : 7.000
## Mean : 815.2 Mean : 9.339
## 3rd Qu.:1165.8 3rd Qu.:14.000
## Max. :1499.0 Max. :29.000
##
## Education EducationField EmployeeCount EmployeeNumber
## Min. :1.000 Human Resources : 15 Min. :1 Min. : 1.0
## 1st Qu.:2.000 Life Sciences :358 1st Qu.:1 1st Qu.: 477.2
## Median :3.000 Marketing :100 Median :1 Median :1039.0
## Mean :2.901 Medical :270 Mean :1 Mean :1029.8
## 3rd Qu.:4.000 Other : 52 3rd Qu.:1 3rd Qu.:1561.5
## Max. :5.000 Technical Degree: 75 Max. :1 Max. :2064.0
##
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement
## Min. :1.000 Female:354 Min. : 30.00 Min. :1.000
## 1st Qu.:2.000 Male :516 1st Qu.: 48.00 1st Qu.:2.000
## Median :3.000 Median : 66.00 Median :3.000
## Mean :2.701 Mean : 65.61 Mean :2.723
## 3rd Qu.:4.000 3rd Qu.: 83.00 3rd Qu.:3.000
## Max. :4.000 Max. :100.00 Max. :4.000
##
## JobLevel JobRole JobSatisfaction
## Min. :1.000 Sales Executive :200 Min. :1.000
## 1st Qu.:1.000 Research Scientist :172 1st Qu.:2.000
## Median :2.000 Laboratory Technician :153 Median :3.000
## Mean :2.039 Manufacturing Director : 87 Mean :2.709
## 3rd Qu.:3.000 Healthcare Representative: 76 3rd Qu.:4.000
## Max. :5.000 Sales Representative : 53 Max. :4.000
## (Other) :129
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18
## Divorced:191 Min. : 1081 Min. : 2094 Min. :0.000 Y:870
## Married :410 1st Qu.: 2840 1st Qu.: 8092 1st Qu.:1.000
## Single :269 Median : 4946 Median :14074 Median :2.000
## Mean : 6390 Mean :14326 Mean :2.728
## 3rd Qu.: 8182 3rd Qu.:20456 3rd Qu.:4.000
## Max. :19999 Max. :26997 Max. :9.000
##
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## No :618 Min. :11.0 Min. :3.000 Min. :1.000
## Yes:252 1st Qu.:12.0 1st Qu.:3.000 1st Qu.:2.000
## Median :14.0 Median :3.000 Median :3.000
## Mean :15.2 Mean :3.152 Mean :2.707
## 3rd Qu.:18.0 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :25.0 Max. :4.000 Max. :4.000
##
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## Min. :80 Min. :0.0000 Min. : 0.00 Min. :0.000
## 1st Qu.:80 1st Qu.:0.0000 1st Qu.: 6.00 1st Qu.:2.000
## Median :80 Median :1.0000 Median :10.00 Median :3.000
## Mean :80 Mean :0.7839 Mean :11.05 Mean :2.832
## 3rd Qu.:80 3rd Qu.:1.0000 3rd Qu.:15.00 3rd Qu.:3.000
## Max. :80 Max. :3.0000 Max. :40.00 Max. :6.000
##
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Min. :1.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:2.000 1st Qu.: 3.000 1st Qu.: 2.000
## Median :3.000 Median : 5.000 Median : 3.000
## Mean :2.782 Mean : 6.962 Mean : 4.205
## 3rd Qu.:3.000 3rd Qu.:10.000 3rd Qu.: 7.000
## Max. :4.000 Max. :40.000 Max. :18.000
##
## YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 2.00
## Median : 1.000 Median : 3.00
## Mean : 2.169 Mean : 4.14
## 3rd Qu.: 3.000 3rd Qu.: 7.00
## Max. :15.000 Max. :17.00
## colnames(DDSAnalytics)## [1] "ID" "Age"
## [3] "Attrition" "BusinessTravel"
## [5] "DailyRate" "Department"
## [7] "DistanceFromHome" "Education"
## [9] "EducationField" "EmployeeCount"
## [11] "EmployeeNumber" "EnvironmentSatisfaction"
## [13] "Gender" "HourlyRate"
## [15] "JobInvolvement" "JobLevel"
## [17] "JobRole" "JobSatisfaction"
## [19] "MaritalStatus" "MonthlyIncome"
## [21] "MonthlyRate" "NumCompaniesWorked"
## [23] "Over18" "OverTime"
## [25] "PercentSalaryHike" "PerformanceRating"
## [27] "RelationshipSatisfaction" "StandardHours"
## [29] "StockOptionLevel" "TotalWorkingYears"
## [31] "TrainingTimesLastYear" "WorkLifeBalance"
## [33] "YearsAtCompany" "YearsInCurrentRole"
## [35] "YearsSinceLastPromotion" "YearsWithCurrManager"dim(DDSAnalytics)## [1] 870 36str(DDSAnalytics)## 'data.frame': 870 obs. of 36 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 32 40 35 32 24 27 41 37 34 34 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 2 3 2 2 3 3 3 2 ...
## $ DailyRate : int 117 1308 200 801 567 294 1283 309 1333 653 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 3 2 2 2 3 3 2 ...
## $ DistanceFromHome : int 13 14 18 1 2 10 5 10 10 10 ...
## $ Education : int 4 3 2 4 1 2 5 4 4 4 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 4 2 3 6 2 4 2 2 6 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 859 1128 1412 2016 1646 733 1448 1105 1055 1597 ...
## $ EnvironmentSatisfaction : int 2 3 3 3 1 4 2 4 3 4 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 1 1 2 ...
## $ HourlyRate : int 73 44 60 48 32 32 90 88 87 92 ...
## $ JobInvolvement : int 3 2 3 3 3 3 4 2 3 2 ...
## $ JobLevel : int 2 5 3 3 1 3 1 2 1 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 6 5 8 7 5 7 8 9 1 ...
## $ JobSatisfaction : int 4 3 4 4 4 1 3 4 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 1 3 3 2 3 1 2 1 2 2 ...
## $ MonthlyIncome : int 4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
## $ MonthlyRate : int 9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
## $ NumCompaniesWorked : int 2 1 2 1 1 1 2 2 1 1 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 2 2 1 ...
## $ PercentSalaryHike : int 11 14 11 19 13 21 12 14 19 14 ...
## $ PerformanceRating : int 3 3 3 3 3 4 3 3 3 3 ...
## $ RelationshipSatisfaction: int 3 1 3 3 3 3 1 3 4 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 1 0 0 2 0 2 0 3 1 1 ...
## $ TotalWorkingYears : int 8 21 10 14 6 9 7 8 1 8 ...
## $ TrainingTimesLastYear : int 3 2 2 3 2 4 5 5 2 3 ...
## $ WorkLifeBalance : int 2 4 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 5 20 2 14 6 9 4 1 1 8 ...
## $ YearsInCurrentRole : int 2 7 2 10 3 7 2 0 1 2 ...
## $ YearsSinceLastPromotion : int 0 4 2 5 1 1 0 0 0 7 ...
## $ YearsWithCurrManager : int 3 9 2 7 3 7 3 0 0 7 ...EDA Section
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany, y=MonthlyIncome,color=JobRole))+
geom_point()+ggtitle("Year at Company vs. Monthly Income")
# Bar plot of employees by thier Job role.
summary(DDSAnalytics$JobRole)## Healthcare Representative Human Resources
## 76 27
## Laboratory Technician Manager
## 153 51
## Manufacturing Director Research Director
## 87 51
## Research Scientist Sales Executive
## 172 200
## Sales Representative
## 53jobRoleTable <- table(DDSAnalytics$JobRole)
jobRoleTableDf <- data.frame(jobRoleTable)
names(jobRoleTableDf) <- c("JobRole","TotalCount")
#What we would like is for R to respect the order in data.frame. For that to happen, we need to change the order of factor levels by specifying the order explicitly.
jobRoleTableDf$JobRole <- factor(jobRoleTableDf$JobRole,levels = jobRoleTableDf$JobRole[order(jobRoleTableDf$TotalCount)])
# library(dplyr)
# jobRoleTableDf%>%ggplot(aes(x=JobRole,y=TotalCount,fill=Jobsatisfaction))+geom_bar(stat = "identity")+coord_flip()+ggtitle("Number of Employe by Job Role")+
# xlab("Job Role")+ylab("Total number of Employees")+theme(
# plot.title = element_text(color = "black",
# hjust = 0.5,
# size = 15),
# legend.position = "none"
# )
DDSAnalytics%>%ggplot(aes(x=Department,fill=Department))+geom_bar()
DDSAnalytics%>%ggplot(aes(x=Education,fill=EducationField))+geom_bar()
DDSAnalytics%>%ggplot(aes(x=JobSatisfaction,fill=EducationField))+geom_bar()
# Job satisfaction is higher for Life Science field.
# Job Satisfaction higher for employees with the job title Sales executive.
# there are very few employee in Job satisfaction level 4 with job title Human Resource. DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=JobSatisfaction,fill=JobRole))+
geom_bar(stat = "identity")
#jobSatisfaction
plyr::count(DDSAnalytics,'JobSatisfaction')->jobSatisfactionFreq
data.frame(jobSatisfactionFreq)## JobSatisfaction freq
## 1 1 179
## 2 2 166
## 3 3 254
## 4 4 271jobSatisfactionFreq%>%ggplot(aes(x=JobSatisfaction,y=freq))+
geom_bar(stat = "identity")
# Over all the employee are satisfied with their job.
hist(DDSAnalytics$MonthlyIncome)
hist(DDSAnalytics$MonthlyRate)
hist(DDSAnalytics$YearsAtCompany)
hist(DDSAnalytics$YearsInCurrentRole)
hist(DDSAnalytics$TotalWorkingYears)
hist(DDSAnalytics$DistanceFromHome)
DDSAnalytics%>%ggplot(aes(x=JobSatisfaction,y=YearsAtCompany,color=JobRole))+geom_point()
str(DDSAnalytics$Age)## int [1:870] 32 40 35 32 24 27 41 37 34 34 ...DDSAnalytics$Age## [1] 32 40 35 32 24 27 41 37 34 34 43 28 35 30 46 31 32 46 34 44 36 48 43
## [24] 31 33 44 38 33 53 52 32 33 46 35 32 32 35 36 56 31 58 45 49 42 23 42
## [47] 32 38 18 27 38 58 44 45 31 41 42 29 37 25 36 36 31 18 24 32 35 23 35
## [70] 37 41 28 35 41 52 30 34 46 45 31 37 35 38 37 36 59 47 35 27 46 38 29
## [93] 37 35 50 42 46 37 29 34 27 31 34 23 53 26 29 55 46 32 28 52 52 35 35
## [116] 37 25 29 41 34 36 21 45 38 29 35 32 45 43 46 29 49 42 39 43 40 29 54
## [139] 39 37 56 58 46 37 40 29 33 33 24 28 31 32 32 41 34 30 40 39 43 42 30
## [162] 28 33 42 35 20 49 33 52 29 30 47 46 33 53 21 55 26 32 38 35 38 43 39
## [185] 39 28 25 47 50 28 30 31 46 48 26 51 51 49 44 24 40 32 32 28 32 44 43
## [208] 38 30 41 41 42 37 42 27 40 38 48 30 26 34 45 33 28 38 30 35 22 24 49
## [231] 45 35 47 54 43 37 42 32 25 50 42 45 29 25 29 29 31 32 41 41 33 36 45
## [254] 31 40 43 31 30 34 25 31 29 33 29 24 35 34 32 33 30 45 34 27 36 55 56
## [277] 22 43 25 55 43 41 40 31 38 33 22 34 36 35 45 36 27 47 40 35 34 34 31
## [300] 28 32 35 55 35 58 24 50 34 55 25 34 40 31 51 60 47 34 24 39 19 32 34
## [323] 30 46 39 27 44 42 38 42 49 32 44 27 59 33 35 34 50 27 36 28 33 30 30
## [346] 39 34 26 44 56 42 31 40 40 41 34 26 29 32 36 52 29 52 40 55 56 34 40
## [369] 23 32 31 30 45 37 20 50 36 42 29 30 47 26 48 23 51 38 35 29 36 21 34
## [392] 26 31 60 42 31 46 34 56 44 30 42 47 36 35 43 31 36 53 43 36 41 37 32
## [415] 34 35 30 36 37 29 34 37 31 18 28 43 30 21 32 34 27 40 49 29 40 27 28
## [438] 26 50 38 32 29 28 44 46 47 38 42 38 45 33 44 35 33 48 40 31 51 56 36
## [461] 42 36 50 31 43 38 36 36 26 53 27 29 45 45 28 46 37 26 52 37 29 31 34
## [484] 56 58 41 30 31 48 34 49 50 29 35 36 51 45 37 36 39 58 28 48 29 47 32
## [507] 33 51 37 41 38 39 32 33 21 51 25 42 30 56 34 28 45 49 59 53 45 31 35
## [530] 56 38 36 29 32 29 43 38 42 24 42 30 33 27 38 34 34 49 40 38 45 41 28
## [553] 36 48 40 37 54 34 42 26 36 58 27 31 30 44 32 30 42 50 41 21 28 34 26
## [576] 37 38 44 39 29 25 42 38 28 44 40 38 36 37 35 22 34 28 49 40 33 35 40
## [599] 27 24 24 32 30 29 29 53 59 38 26 33 35 41 33 19 30 34 33 44 39 35 20
## [622] 47 34 35 28 27 24 38 55 34 55 34 38 36 43 35 42 34 24 27 30 40 55 34
## [645] 41 40 18 26 35 40 35 60 40 48 22 26 51 54 41 36 32 34 46 35 34 29 29
## [668] 33 32 54 37 33 28 35 27 46 36 40 36 31 25 43 36 57 28 31 29 33 19 38
## [691] 38 47 42 30 37 47 48 36 18 46 29 34 35 26 59 41 54 31 38 50 30 57 48
## [714] 36 36 24 30 28 35 28 37 37 34 36 42 25 37 34 26 22 43 39 27 32 34 27
## [737] 39 59 35 35 25 31 37 23 25 24 29 34 24 33 20 31 50 46 54 44 35 49 38
## [760] 45 29 47 41 50 34 45 41 40 46 45 33 46 39 36 42 23 30 32 26 29 41 39
## [783] 33 33 47 31 51 52 55 25 49 34 38 36 49 33 45 22 19 36 33 31 39 35 44
## [806] 36 25 22 30 27 30 25 24 47 38 33 30 33 30 28 32 29 34 31 36 46 31 34
## [829] 38 50 45 19 28 19 55 37 37 34 47 48 18 29 52 34 34 28 19 39 40 26 31
## [852] 30 29 28 35 35 30 31 41 44 51 43 34 47 45 48 32 47 45 35summary(DDSAnalytics$Age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.00 30.00 35.00 36.83 43.00 60.00hist(DDSAnalytics$Age)
#employee training programs, identifying high-potential employees and reducing/preventing voluntary employee turnover (attrition).
as.factor(DDSAnalytics$TrainingTimesLastYear)->traingTimeFactor
as.factor(DDSAnalytics$JobInvolvement)->jobInvFactor
dfTrainTimJobInv <- data.frame(JobInv=DDSAnalytics$JobInvolvement,
Training=traingTimeFactor,
EmployeeJobSatisfaction=DDSAnalytics$JobSatisfaction,
jobInvFac= jobInvFactor)
#dfTrainTimJobInv%>%ggplot(aes(x=JobInv))+geom_bar(aes(fill=Training))
library(GGally)
dfTrainTimJobInv%>%dplyr::select(JobInv,Training,EmployeeJobSatisfaction)%>%
ggpairs(aes(color=Training))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
dfTrainTimJobInv%>%ggplot(aes(Training))+geom_bar(aes(fill=jobInvFac)) ->df1#EDA, Correlation and Variable Selection
#Correlation
DDSAnalytics%>%dplyr::select(Age, DailyRate,DistanceFromHome,
Education, EnvironmentSatisfaction,
HourlyRate,JobInvolvement,JobLevel,
JobSatisfaction, MonthlyIncome,MonthlyRate,
NumCompaniesWorked,PerformanceRating,TotalWorkingYears,
RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
WorkLifeBalance,YearsInCurrentRole, YearsSinceLastPromotion,
YearsAtCompany, YearsWithCurrManager,
PercentSalaryHike,HourlyRate)->corVarSelection
#cor(k,method = "pearson")
#corrplot(DDSAnalytics, method = "number")
#k2 <- rcorr(as.matrix(k))
library(corrplot)
cor_selected_Var <-cor(corVarSelection)
head(round(cor_selected_Var,2))## Age DailyRate DistanceFromHome Education
## Age 1.00 0.01 0.01 0.22
## DailyRate 0.01 1.00 0.01 -0.01
## DistanceFromHome 0.01 0.01 1.00 0.05
## Education 0.22 -0.01 0.05 1.00
## EnvironmentSatisfaction -0.01 -0.01 -0.04 -0.04
## HourlyRate 0.05 0.05 0.07 0.01
## EnvironmentSatisfaction HourlyRate JobInvolvement
## Age -0.01 0.05 0.02
## DailyRate -0.01 0.05 0.06
## DistanceFromHome -0.04 0.07 0.00
## Education -0.04 0.01 0.03
## EnvironmentSatisfaction 1.00 -0.03 0.00
## HourlyRate -0.03 1.00 0.07
## JobLevel JobSatisfaction MonthlyIncome MonthlyRate
## Age 0.48 -0.02 0.48 0.07
## DailyRate 0.00 0.00 0.00 -0.03
## DistanceFromHome 0.02 -0.02 -0.01 -0.01
## Education 0.13 0.01 0.13 -0.02
## EnvironmentSatisfaction 0.00 -0.02 -0.02 0.06
## HourlyRate -0.01 -0.09 0.00 -0.02
## NumCompaniesWorked PerformanceRating
## Age 0.29 -0.04
## DailyRate 0.05 -0.02
## DistanceFromHome -0.05 0.03
## Education 0.16 -0.03
## EnvironmentSatisfaction 0.01 0.00
## HourlyRate 0.01 0.00
## TotalWorkingYears RelationshipSatisfaction
## Age 0.65 -0.01
## DailyRate -0.01 0.01
## DistanceFromHome 0.00 0.04
## Education 0.15 -0.03
## EnvironmentSatisfaction -0.02 0.00
## HourlyRate 0.03 0.02
## StockOptionLevel TrainingTimesLastYear
## Age 0.04 -0.05
## DailyRate 0.02 -0.01
## DistanceFromHome 0.07 -0.04
## Education 0.03 -0.06
## EnvironmentSatisfaction 0.03 -0.01
## HourlyRate 0.06 0.01
## WorkLifeBalance YearsInCurrentRole
## Age -0.01 0.21
## DailyRate -0.03 0.00
## DistanceFromHome -0.01 -0.01
## Education 0.01 0.06
## EnvironmentSatisfaction 0.08 0.02
## HourlyRate -0.03 0.00
## YearsSinceLastPromotion YearsAtCompany
## Age 0.22 0.29
## DailyRate -0.06 -0.04
## DistanceFromHome -0.02 -0.02
## Education 0.07 0.06
## EnvironmentSatisfaction 0.01 -0.02
## HourlyRate 0.01 0.00
## YearsWithCurrManager PercentSalaryHike
## Age 0.19 -0.03
## DailyRate -0.02 0.03
## DistanceFromHome -0.02 0.05
## Education 0.09 0.00
## EnvironmentSatisfaction -0.02 0.00
## HourlyRate 0.00 -0.02corrplot(cor_selected_Var,method = "circle")
corrplot(cor_selected_Var,method = "number") # correlation check for all variables 
# Displays diagnoal histogram
library(PerformanceAnalytics)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:data.table':
##
## first, last## The following objects are masked from 'package:gdata':
##
## first, last## The following objects are masked from 'package:dplyr':
##
## first, last##
## Attaching package: 'PerformanceAnalytics'## The following objects are masked from 'package:e1071':
##
## kurtosis, skewness## The following object is masked from 'package:graphics':
##
## legendchart.Correlation(cor_selected_Var,histogram = TRUE, pac=19)
DDSAnalytics%>%ggplot(aes(x=Age,y=TotalWorkingYears,color=Gender))+geom_point()
DDSAnalytics%>%ggplot(aes(x=JobLevel,y=MonthlyIncome,color=Department))+geom_point()
DDSAnalytics%>%ggplot(aes(x=JobLevel,y=TotalWorkingYears))+geom_point()
DDSAnalytics%>%ggplot(aes(x=TotalWorkingYears,y=MonthlyIncome,color=Gender))+geom_point()
DDSAnalytics%>%ggplot(aes(x=YearsInCurrentRole,y=YearsWithCurrManager,color=Gender))+geom_point()
DDSAnalytics%>%ggplot(aes(x=YearsSinceLastPromotion,y=YearsAtCompany,color=Gender))+geom_point()
DDSAnalytics%>%dplyr::select(Age,MonthlyIncome,TotalWorkingYears,PercentSalaryHike,YearsWithCurrManager,
YearsInCurrentRole,YearsSinceLastPromotion,MonthlyRate,NumCompaniesWorked,
RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
WorkLifeBalance,YearsAtCompany)->selected_var
selected_var_correlation <- cor(selected_var)
head(round(selected_var_correlation,2))## Age MonthlyIncome TotalWorkingYears
## Age 1.00 0.48 0.65
## MonthlyIncome 0.48 1.00 0.78
## TotalWorkingYears 0.65 0.78 1.00
## PercentSalaryHike -0.03 -0.05 -0.06
## YearsWithCurrManager 0.19 0.33 0.46
## YearsInCurrentRole 0.21 0.36 0.49
## PercentSalaryHike YearsWithCurrManager
## Age -0.03 0.19
## MonthlyIncome -0.05 0.33
## TotalWorkingYears -0.06 0.46
## PercentSalaryHike 1.00 -0.05
## YearsWithCurrManager -0.05 1.00
## YearsInCurrentRole -0.02 0.71
## YearsInCurrentRole YearsSinceLastPromotion
## Age 0.21 0.22
## MonthlyIncome 0.36 0.32
## TotalWorkingYears 0.49 0.45
## PercentSalaryHike -0.02 -0.07
## YearsWithCurrManager 0.71 0.51
## YearsInCurrentRole 1.00 0.55
## MonthlyRate NumCompaniesWorked
## Age 0.07 0.29
## MonthlyIncome 0.06 0.16
## TotalWorkingYears 0.06 0.26
## PercentSalaryHike 0.00 -0.02
## YearsWithCurrManager -0.02 -0.12
## YearsInCurrentRole 0.03 -0.10
## RelationshipSatisfaction StockOptionLevel
## Age -0.01 0.04
## MonthlyIncome 0.00 0.02
## TotalWorkingYears -0.02 0.04
## PercentSalaryHike -0.05 0.00
## YearsWithCurrManager -0.02 0.04
## YearsInCurrentRole 0.00 0.08
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Age -0.05 -0.01 0.29
## MonthlyIncome -0.04 0.02 0.49
## TotalWorkingYears -0.04 0.02 0.64
## PercentSalaryHike 0.00 0.01 -0.06
## YearsWithCurrManager 0.00 0.02 0.77
## YearsInCurrentRole -0.02 0.08 0.78corrplot(selected_var_correlation,method = "circle")
corrplot(selected_var_correlation,method = "number")
# selected variables with no correlation
DDSAnalytics%>%dplyr::select(Age,PercentSalaryHike,MonthlyRate,NumCompaniesWorked,
RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
WorkLifeBalance,YearsAtCompany,DistanceFromHome,
Education,JobInvolvement,HourlyRate)->selected_var_correlation1
# selected_var_correlation1$MonthlyIncome_log = log(selected_var_correlation1$MonthlyIncome)
# selected_var_correlation1$MonthlyRate_log = log(selected_var_correlation1$MonthlyRate) # log made it left swequed.
selected_var_correlation1_cor <- cor(selected_var_correlation1)
head(round(selected_var_correlation1_cor,2))## Age PercentSalaryHike MonthlyRate
## Age 1.00 -0.03 0.07
## PercentSalaryHike -0.03 1.00 0.00
## MonthlyRate 0.07 0.00 1.00
## NumCompaniesWorked 0.29 -0.02 0.02
## RelationshipSatisfaction -0.01 -0.05 -0.02
## StockOptionLevel 0.04 0.00 -0.04
## NumCompaniesWorked RelationshipSatisfaction
## Age 0.29 -0.01
## PercentSalaryHike -0.02 -0.05
## MonthlyRate 0.02 -0.02
## NumCompaniesWorked 1.00 0.04
## RelationshipSatisfaction 0.04 1.00
## StockOptionLevel 0.03 -0.03
## StockOptionLevel TrainingTimesLastYear
## Age 0.04 -0.05
## PercentSalaryHike 0.00 0.00
## MonthlyRate -0.04 -0.01
## NumCompaniesWorked 0.03 -0.07
## RelationshipSatisfaction -0.03 0.02
## StockOptionLevel 1.00 0.02
## WorkLifeBalance YearsAtCompany DistanceFromHome
## Age -0.01 0.29 0.01
## PercentSalaryHike 0.01 -0.06 0.05
## MonthlyRate 0.01 -0.02 -0.01
## NumCompaniesWorked 0.02 -0.14 -0.05
## RelationshipSatisfaction 0.04 0.01 0.04
## StockOptionLevel 0.05 0.03 0.07
## Education JobInvolvement HourlyRate
## Age 0.22 0.02 0.05
## PercentSalaryHike 0.00 0.01 -0.02
## MonthlyRate -0.02 -0.02 -0.02
## NumCompaniesWorked 0.16 -0.01 0.01
## RelationshipSatisfaction -0.03 0.02 0.02
## StockOptionLevel 0.03 0.07 0.06corrplot(selected_var_correlation1_cor,method = "number")
chart.Correlation(selected_var_correlation1_cor,histogram = TRUE, pac=19)
library(ggpubr)## Loading required package: magrittr##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extract##
## Attaching package: 'ggpubr'## The following object is masked from 'package:cowplot':
##
## get_legend## The following objects are masked from 'package:qwraps2':
##
## mean_ci, mean_sd, median_iqr#Variable Selection and Model Fitting
library(MASS)
#DDSAnalytics <- read.csv(file.choose())
# Fit the model
fitMod <- lm(MonthlyIncome~Age+DailyRate+PercentSalaryHike+MonthlyRate+NumCompaniesWorked+
RelationshipSatisfaction+StockOptionLevel+TrainingTimesLastYear+
WorkLifeBalance+YearsAtCompany+DistanceFromHome+
Education+JobInvolvement+HourlyRate,data = DDSAnalytics)
# Stepwise variable selection
#Variables selected are: Age MonthlyRate NumCompaniesWorked YearsAtCompany
modStepw <- stepAIC(fitMod,direction = "both")## Start: AIC=14279.99
## MonthlyIncome ~ Age + DailyRate + PercentSalaryHike + MonthlyRate +
## NumCompaniesWorked + RelationshipSatisfaction + StockOptionLevel +
## TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany +
## DistanceFromHome + Education + JobInvolvement + HourlyRate
##
## Df Sum of Sq RSS AIC
## - DailyRate 1 1063345 1.1298e+10 14278
## - DistanceFromHome 1 1120616 1.1298e+10 14278
## - RelationshipSatisfaction 1 2121077 1.1299e+10 14278
## - Education 1 2140131 1.1299e+10 14278
## - WorkLifeBalance 1 2206959 1.1299e+10 14278
## - StockOptionLevel 1 2264705 1.1299e+10 14278
## - JobInvolvement 1 4092665 1.1301e+10 14278
## - HourlyRate 1 4993530 1.1302e+10 14278
## - PercentSalaryHike 1 6789217 1.1304e+10 14278
## - TrainingTimesLastYear 1 8966200 1.1306e+10 14279
## <none> 1.1297e+10 14280
## - MonthlyRate 1 39077410 1.1336e+10 14281
## - NumCompaniesWorked 1 206505811 1.1503e+10 14294
## - Age 1 1483997780 1.2781e+10 14385
## - YearsAtCompany 1 2679215399 1.3976e+10 14463
##
## Step: AIC=14278.08
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## RelationshipSatisfaction + StockOptionLevel + TrainingTimesLastYear +
## WorkLifeBalance + YearsAtCompany + DistanceFromHome + Education +
## JobInvolvement + HourlyRate
##
## Df Sum of Sq RSS AIC
## - DistanceFromHome 1 1145851 1.1299e+10 14276
## - Education 1 2064590 1.1300e+10 14276
## - RelationshipSatisfaction 1 2086685 1.1300e+10 14276
## - WorkLifeBalance 1 2117587 1.1300e+10 14276
## - StockOptionLevel 1 2214071 1.1300e+10 14276
## - JobInvolvement 1 4337161 1.1302e+10 14276
## - HourlyRate 1 4814904 1.1303e+10 14276
## - PercentSalaryHike 1 6644296 1.1305e+10 14277
## - TrainingTimesLastYear 1 9007046 1.1307e+10 14277
## <none> 1.1298e+10 14278
## - MonthlyRate 1 38752514 1.1337e+10 14279
## + DailyRate 1 1063345 1.1297e+10 14280
## - NumCompaniesWorked 1 208285019 1.1506e+10 14292
## - Age 1 1484842711 1.2783e+10 14384
## - YearsAtCompany 1 2678231646 1.3976e+10 14461
##
## Step: AIC=14276.16
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## RelationshipSatisfaction + StockOptionLevel + TrainingTimesLastYear +
## WorkLifeBalance + YearsAtCompany + Education + JobInvolvement +
## HourlyRate
##
## Df Sum of Sq RSS AIC
## - RelationshipSatisfaction 1 1947945 1.1301e+10 14274
## - StockOptionLevel 1 1997929 1.1301e+10 14274
## - WorkLifeBalance 1 2073908 1.1301e+10 14274
## - Education 1 2244098 1.1301e+10 14274
## - JobInvolvement 1 4246236 1.1303e+10 14274
## - HourlyRate 1 4538008 1.1304e+10 14274
## - PercentSalaryHike 1 6359489 1.1305e+10 14275
## - TrainingTimesLastYear 1 9294642 1.1308e+10 14275
## <none> 1.1299e+10 14276
## - MonthlyRate 1 38700725 1.1338e+10 14277
## + DistanceFromHome 1 1145851 1.1298e+10 14278
## + DailyRate 1 1088580 1.1298e+10 14278
## - NumCompaniesWorked 1 207140743 1.1506e+10 14290
## - Age 1 1487126021 1.2786e+10 14382
## - YearsAtCompany 1 2677897927 1.3977e+10 14459
##
## Step: AIC=14274.31
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## StockOptionLevel + TrainingTimesLastYear + WorkLifeBalance +
## YearsAtCompany + Education + JobInvolvement + HourlyRate
##
## Df Sum of Sq RSS AIC
## - StockOptionLevel 1 1848939 1.1303e+10 14272
## - WorkLifeBalance 1 1928217 1.1303e+10 14272
## - Education 1 2376371 1.1303e+10 14272
## - JobInvolvement 1 4126458 1.1305e+10 14273
## - HourlyRate 1 4636501 1.1306e+10 14273
## - PercentSalaryHike 1 6063184 1.1307e+10 14273
## - TrainingTimesLastYear 1 9437706 1.1311e+10 14273
## <none> 1.1301e+10 14274
## - MonthlyRate 1 39151712 1.1340e+10 14275
## + RelationshipSatisfaction 1 1947945 1.1299e+10 14276
## + DailyRate 1 1053348 1.1300e+10 14276
## + DistanceFromHome 1 1007112 1.1300e+10 14276
## - NumCompaniesWorked 1 205607025 1.1507e+10 14288
## - Age 1 1489380826 1.2790e+10 14380
## - YearsAtCompany 1 2676081706 1.3977e+10 14457
##
## Step: AIC=14272.46
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany +
## Education + JobInvolvement + HourlyRate
##
## Df Sum of Sq RSS AIC
## - WorkLifeBalance 1 1759117 1.1305e+10 14271
## - Education 1 2308802 1.1305e+10 14271
## - JobInvolvement 1 3777372 1.1307e+10 14271
## - HourlyRate 1 5002592 1.1308e+10 14271
## - PercentSalaryHike 1 6049458 1.1309e+10 14271
## - TrainingTimesLastYear 1 9615796 1.1313e+10 14271
## <none> 1.1303e+10 14272
## - MonthlyRate 1 39818844 1.1343e+10 14274
## + StockOptionLevel 1 1848939 1.1301e+10 14274
## + RelationshipSatisfaction 1 1798956 1.1301e+10 14274
## + DailyRate 1 1006075 1.1302e+10 14274
## + DistanceFromHome 1 817380 1.1302e+10 14274
## - NumCompaniesWorked 1 204777164 1.1508e+10 14286
## - Age 1 1487744081 1.2791e+10 14378
## - YearsAtCompany 1 2674278411 1.3977e+10 14455
##
## Step: AIC=14270.59
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + YearsAtCompany + Education + JobInvolvement +
## HourlyRate
##
## Df Sum of Sq RSS AIC
## - Education 1 2349298 1.1307e+10 14269
## - JobInvolvement 1 3842262 1.1309e+10 14269
## - HourlyRate 1 5153532 1.1310e+10 14269
## - PercentSalaryHike 1 5990888 1.1311e+10 14269
## - TrainingTimesLastYear 1 9432510 1.1314e+10 14269
## <none> 1.1305e+10 14271
## - MonthlyRate 1 39984889 1.1345e+10 14272
## + WorkLifeBalance 1 1759117 1.1303e+10 14272
## + StockOptionLevel 1 1679840 1.1303e+10 14272
## + RelationshipSatisfaction 1 1671684 1.1303e+10 14272
## + DailyRate 1 929403 1.1304e+10 14272
## + DistanceFromHome 1 795656 1.1304e+10 14272
## - NumCompaniesWorked 1 206424162 1.1511e+10 14284
## - Age 1 1485987397 1.2791e+10 14376
## - YearsAtCompany 1 2684987116 1.3990e+10 14454
##
## Step: AIC=14268.77
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + YearsAtCompany + JobInvolvement +
## HourlyRate
##
## Df Sum of Sq RSS AIC
## - JobInvolvement 1 4032699 1.1311e+10 14267
## - HourlyRate 1 5198903 1.1312e+10 14267
## - PercentSalaryHike 1 5939200 1.1313e+10 14267
## - TrainingTimesLastYear 1 9923213 1.1317e+10 14268
## <none> 1.1307e+10 14269
## - MonthlyRate 1 39351114 1.1346e+10 14270
## + Education 1 2349298 1.1305e+10 14271
## + WorkLifeBalance 1 1799613 1.1305e+10 14271
## + RelationshipSatisfaction 1 1794779 1.1305e+10 14271
## + StockOptionLevel 1 1613162 1.1305e+10 14271
## + DistanceFromHome 1 949046 1.1306e+10 14271
## + DailyRate 1 854948 1.1306e+10 14271
## - NumCompaniesWorked 1 213428005 1.1520e+10 14283
## - Age 1 1544879419 1.2852e+10 14378
## - YearsAtCompany 1 2691269215 1.3998e+10 14452
##
## Step: AIC=14267.08
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + YearsAtCompany + HourlyRate
##
## Df Sum of Sq RSS AIC
## - HourlyRate 1 4608935 1.1316e+10 14265
## - PercentSalaryHike 1 5810127 1.1317e+10 14266
## - TrainingTimesLastYear 1 10211076 1.1321e+10 14266
## <none> 1.1311e+10 14267
## - MonthlyRate 1 38847658 1.1350e+10 14268
## + JobInvolvement 1 4032699 1.1307e+10 14269
## + Education 1 2539735 1.1309e+10 14269
## + WorkLifeBalance 1 1868554 1.1309e+10 14269
## + RelationshipSatisfaction 1 1697863 1.1309e+10 14269
## + StockOptionLevel 1 1273880 1.1310e+10 14269
## + DailyRate 1 1068020 1.1310e+10 14269
## + DistanceFromHome 1 896886 1.1310e+10 14269
## - NumCompaniesWorked 1 212128122 1.1523e+10 14281
## - Age 1 1551816172 1.2863e+10 14377
## - YearsAtCompany 1 2687657896 1.3999e+10 14451
##
## Step: AIC=14265.44
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + YearsAtCompany
##
## Df Sum of Sq RSS AIC
## - PercentSalaryHike 1 5586325 1.1321e+10 14264
## - TrainingTimesLastYear 1 10326155 1.1326e+10 14264
## <none> 1.1316e+10 14265
## - MonthlyRate 1 39414999 1.1355e+10 14266
## + HourlyRate 1 4608935 1.1311e+10 14267
## + JobInvolvement 1 3442732 1.1312e+10 14267
## + Education 1 2569654 1.1313e+10 14267
## + WorkLifeBalance 1 2010778 1.1314e+10 14267
## + RelationshipSatisfaction 1 1780010 1.1314e+10 14267
## + StockOptionLevel 1 1586224 1.1314e+10 14267
## + DailyRate 1 864891 1.1315e+10 14267
## + DistanceFromHome 1 639252 1.1315e+10 14267
## - NumCompaniesWorked 1 212501684 1.1528e+10 14280
## - Age 1 1547207287 1.2863e+10 14375
## - YearsAtCompany 1 2692826523 1.4009e+10 14449
##
## Step: AIC=14263.87
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + TrainingTimesLastYear +
## YearsAtCompany
##
## Df Sum of Sq RSS AIC
## - TrainingTimesLastYear 1 10333102 1.1332e+10 14263
## <none> 1.1321e+10 14264
## - MonthlyRate 1 39373688 1.1361e+10 14265
## + PercentSalaryHike 1 5586325 1.1316e+10 14265
## + HourlyRate 1 4385133 1.1317e+10 14266
## + JobInvolvement 1 3339411 1.1318e+10 14266
## + Education 1 2513386 1.1319e+10 14266
## + WorkLifeBalance 1 1945145 1.1319e+10 14266
## + StockOptionLevel 1 1574609 1.1320e+10 14266
## + RelationshipSatisfaction 1 1509312 1.1320e+10 14266
## + DailyRate 1 748081 1.1321e+10 14266
## + DistanceFromHome 1 456620 1.1321e+10 14266
## - NumCompaniesWorked 1 214556393 1.1536e+10 14278
## - Age 1 1548022772 1.2869e+10 14373
## - YearsAtCompany 1 2715028200 1.4036e+10 14449
##
## Step: AIC=14262.66
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + YearsAtCompany
##
## Df Sum of Sq RSS AIC
## <none> 1.1332e+10 14263
## - MonthlyRate 1 39763836 1.1371e+10 14264
## + TrainingTimesLastYear 1 10333102 1.1321e+10 14264
## + PercentSalaryHike 1 5593272 1.1326e+10 14264
## + HourlyRate 1 4497282 1.1327e+10 14264
## + JobInvolvement 1 3595979 1.1328e+10 14264
## + Education 1 3038550 1.1329e+10 14264
## + WorkLifeBalance 1 1751465 1.1330e+10 14264
## + StockOptionLevel 1 1738488 1.1330e+10 14264
## + RelationshipSatisfaction 1 1651462 1.1330e+10 14264
## + DailyRate 1 785720 1.1331e+10 14265
## + DistanceFromHome 1 653090 1.1331e+10 14265
## - NumCompaniesWorked 1 220217707 1.1552e+10 14277
## - Age 1 1557187987 1.2889e+10 14373
## - YearsAtCompany 1 2709372536 1.4041e+10 14447modStepw$anova## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## MonthlyIncome ~ Age + DailyRate + PercentSalaryHike + MonthlyRate +
## NumCompaniesWorked + RelationshipSatisfaction + StockOptionLevel +
## TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany +
## DistanceFromHome + Education + JobInvolvement + HourlyRate
##
## Final Model:
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + YearsAtCompany
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 855 11296930706 14279.99
## 2 - DailyRate 1 1063345 856 11297994051 14278.08
## 3 - DistanceFromHome 1 1145851 857 11299139902 14276.16
## 4 - RelationshipSatisfaction 1 1947945 858 11301087847 14274.31
## 5 - StockOptionLevel 1 1848939 859 11302936786 14272.46
## 6 - WorkLifeBalance 1 1759117 860 11304695904 14270.59
## 7 - Education 1 2349298 861 11307045202 14268.77
## 8 - JobInvolvement 1 4032699 862 11311077901 14267.08
## 9 - HourlyRate 1 4608935 863 11315686836 14265.44
## 10 - PercentSalaryHike 1 5586325 864 11321273161 14263.87
## 11 - TrainingTimesLastYear 1 10333102 865 11331606263 14262.66# histogram and qqplot of residuals
hist(modStepw$residuals,col="orange",main = "Residual Histogram")
ggqqplot(modStepw$residuals,color = "orange",title = "Residual Normality Check") # qqplot 
plot(modStepw$residuals,col="orange",main = "Residual Plots")
#Cross Validation
# Spliting dataset into Training and Test and Cross Validation
set.seed(5)
ddsObs = sample(seq(1,dim(DDSAnalytics)[1]),round(.75*dim(DDSAnalytics)[1]),replace = FALSE)
ddsTrain = DDSAnalytics[ddsObs,]
ddsTrain## ID Age Attrition BusinessTravel DailyRate Department
## 834 834 19 No Travel_Rarely 645 Research & Development
## 697 697 48 No Travel_Frequently 365 Research & Development
## 207 207 43 Yes Travel_Rarely 1372 Sales
## 715 715 36 No Non-Travel 1434 Sales
## 809 809 30 No Travel_Rarely 793 Research & Development
## 725 725 42 No Travel_Rarely 1142 Research & Development
## 222 222 45 Yes Travel_Frequently 306 Sales
## 71 71 41 No Travel_Rarely 642 Research & Development
## 503 503 48 Yes Travel_Frequently 708 Sales
## 403 403 47 No Travel_Rarely 207 Research & Development
## 387 387 35 No Travel_Frequently 944 Sales
## 806 806 36 No Travel_Rarely 1383 Research & Development
## 826 826 46 No Travel_Rarely 430 Research & Development
## 821 821 32 No Travel_Rarely 634 Research & Development
## 844 844 34 No Travel_Rarely 943 Research & Development
## 528 528 31 No Travel_Rarely 1332 Research & Development
## 128 128 45 No Travel_Rarely 556 Research & Development
## 539 539 24 Yes Travel_Rarely 1448 Sales
## 457 457 31 No Travel_Rarely 1112 Sales
## 732 732 39 Yes Travel_Rarely 1162 Sales
## 842 842 29 No Travel_Rarely 332 Human Resources
## 85 85 36 No Travel_Rarely 429 Research & Development
## 122 122 21 No Travel_Rarely 501 Sales
## 474 474 45 No Travel_Rarely 1316 Research & Development
## 819 819 30 No Travel_Rarely 1427 Research & Development
## 625 625 28 Yes Travel_Frequently 289 Research & Development
## 184 184 39 No Travel_Rarely 1089 Research & Development
## 92 92 29 Yes Travel_Rarely 408 Sales
## 269 269 33 No Travel_Frequently 1303 Research & Development
## 463 463 50 No Travel_Frequently 809 Sales
## 407 407 31 No Travel_Rarely 196 Sales
## 235 235 43 No Travel_Frequently 394 Sales
## 54 54 45 No Non-Travel 805 Research & Development
## 146 146 29 No Non-Travel 746 Sales
## 464 464 31 No Travel_Frequently 163 Research & Development
## 192 192 31 No Travel_Frequently 1125 Sales
## 665 665 34 No Non-Travel 1065 Sales
## 394 394 60 No Travel_Frequently 1499 Sales
## 810 810 27 No Travel_Rarely 1157 Research & Development
## 512 512 39 No Non-Travel 1485 Research & Development
## 628 628 38 No Travel_Rarely 343 Research & Development
## 248 248 32 No Travel_Rarely 234 Sales
## 8 8 37 No Travel_Rarely 309 Sales
## 468 468 36 No Travel_Rarely 132 Research & Development
## 851 851 31 Yes Travel_Rarely 542 Sales
## 404 404 36 No Travel_Rarely 711 Research & Development
## 365 365 55 No Travel_Rarely 282 Research & Development
## 491 491 49 No Travel_Frequently 636 Research & Development
## 616 616 34 No Travel_Rarely 1157 Research & Development
## 748 748 34 No Travel_Frequently 669 Research & Development
## 123 123 45 No Travel_Rarely 930 Sales
## 841 841 18 Yes Travel_Rarely 230 Research & Development
## 326 326 27 No Travel_Rarely 618 Research & Development
## 149 149 24 No Travel_Rarely 477 Research & Development
## 91 91 38 No Travel_Frequently 1490 Research & Development
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## 563 563 27 No Travel_Rarely 1134 Research & Development
## 80 80 31 No Travel_Rarely 1276 Research & Development
## 828 828 34 No Travel_Rarely 131 Sales
## 481 481 29 No Travel_Rarely 136 Research & Development
## 466 466 38 No Travel_Rarely 371 Research & Development
## 510 510 41 Yes Travel_Rarely 1360 Research & Development
## 381 381 47 No Travel_Rarely 1482 Research & Development
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## 127 127 32 Yes Travel_Frequently 1125 Research & Development
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## 527 527 45 No Travel_Rarely 1268 Sales
## 100 100 34 No Travel_Rarely 401 Research & Development
## 209 209 30 No Travel_Rarely 201 Research & Development
## 148 148 33 No Travel_Rarely 1242 Sales
## 125 125 29 No Travel_Frequently 442 Sales
## 754 754 46 Yes Travel_Rarely 1254 Sales
## 139 139 39 Yes Travel_Rarely 1122 Research & Development
## 649 649 35 No Travel_Rarely 776 Sales
## 516 516 51 No Travel_Rarely 1302 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 412 412 41 No Travel_Rarely 918 Sales
## 667 667 29 No Travel_Frequently 1413 Sales
## 227 227 35 No Travel_Rarely 464 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 27 27 38 No Travel_Rarely 1245 Sales
## 240 240 50 No Travel_Rarely 813 Research & Development
## 169 169 52 No Travel_Rarely 956 Research & Development
## 422 422 37 No Non-Travel 1063 Research & Development
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## 188 188 47 No Travel_Rarely 1225 Sales
## 521 521 34 No Travel_Frequently 702 Research & Development
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## 256 256 43 No Non-Travel 343 Research & Development
## 145 145 40 No Travel_Rarely 1202 Research & Development
## 262 262 29 Yes Travel_Rarely 224 Research & Development
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## 135 135 43 No Travel_Frequently 559 Research & Development
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## 374 374 37 No Travel_Rarely 1470 Research & Development
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## 793 793 38 No Travel_Rarely 397 Research & Development
## 17 17 32 No Travel_Rarely 498 Research & Development
## 718 718 28 No Travel_Rarely 857 Research & Development
## 176 176 21 No Travel_Rarely 984 Research & Development
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## 141 141 56 No Travel_Rarely 1369 Research & Development
## 28 28 33 Yes Travel_Rarely 603 Sales
## 423 423 31 Yes Travel_Rarely 249 Sales
## 363 363 52 Yes Travel_Rarely 266 Sales
## 796 796 33 Yes Travel_Frequently 1076 Research & Development
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## 268 268 32 Yes Travel_Rarely 1259 Research & Development
## 272 272 34 No Travel_Rarely 1130 Research & Development
## 671 671 37 No Travel_Rarely 397 Research & Development
## 51 51 38 No Travel_Rarely 362 Research & Development
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## 441 441 32 No Travel_Frequently 1316 Research & Development
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## 87 87 47 No Travel_Rarely 703 Sales
## 575 575 26 No Travel_Rarely 703 Sales
## 414 414 32 No Travel_Frequently 1318 Sales
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## 224 224 28 No Travel_Rarely 1423 Research & Development
## 580 580 29 No Travel_Rarely 1329 Research & Development
## 622 622 47 No Travel_Frequently 1379 Research & Development
## 155 155 34 No Travel_Rarely 546 Research & Development
## 847 847 19 No Travel_Rarely 1181 Research & Development
## 542 542 33 No Travel_Rarely 867 Research & Development
## 395 395 42 No Travel_Frequently 1271 Research & Development
## 67 67 35 No Travel_Rarely 1017 Research & Development
## 25 25 33 No Travel_Rarely 1069 Research & Development
## 288 288 34 No Travel_Rarely 1346 Research & Development
## 358 358 29 Yes Travel_Rarely 408 Research & Development
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## 728 728 34 No Travel_Frequently 878 Research & Development
## 730 730 22 No Travel_Rarely 594 Research & Development
## 303 303 55 No Travel_Rarely 1441 Research & Development
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## 562 562 58 No Travel_Frequently 1216 Research & Development
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## 35 35 32 No Travel_Rarely 1373 Research & Development
## 533 533 29 No Travel_Rarely 1252 Research & Development
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## 243 243 29 No Travel_Rarely 1328 Research & Development
## 707 707 54 No Travel_Rarely 431 Research & Development
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## 774 774 36 Yes Travel_Rarely 318 Research & Development
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## 484 484 56 No Travel_Rarely 713 Research & Development
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## 84 84 37 No Travel_Rarely 1319 Research & Development
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## 118 118 29 No Travel_Rarely 1378 Research & Development
## 261 261 31 Yes Travel_Frequently 703 Sales
## 213 213 37 No Travel_Rarely 1225 Research & Development
## 459 459 56 No Travel_Rarely 1255 Research & Development
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## 599 599 27 No Travel_Rarely 155 Research & Development
## 113 113 52 No Travel_Rarely 994 Research & Development
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## 432 432 40 No Non-Travel 458 Research & Development
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## 440 440 38 No Travel_Rarely 827 Research & Development
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## 2 2 40 No Travel_Rarely 1308 Research & Development
## 216 216 40 No Travel_Rarely 896 Research & Development
## 312 312 40 No Travel_Rarely 302 Research & Development
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## 429 429 32 Yes Travel_Rarely 515 Research & Development
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## 143 143 46 No Travel_Rarely 566 Research & Development
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## 630 630 34 No Travel_Rarely 1442 Research & Development
## 605 605 29 Yes Travel_Rarely 428 Sales
## 462 462 36 No Travel_Rarely 311 Research & Development
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## 287 287 22 Yes Travel_Rarely 391 Research & Development
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## 758 758 49 No Travel_Rarely 1495 Research & Development
## 514 514 33 No Travel_Frequently 515 Research & Development
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## 52 52 58 No Travel_Rarely 1055 Research & Development
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## 815 815 38 No Travel_Rarely 395 Sales
## 341 341 36 No Travel_Rarely 852 Research & Development
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## 662 662 34 No Travel_Rarely 1303 Research & Development
## 7 7 41 No Travel_Rarely 1283 Research & Development
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## 442 442 29 No Travel_Rarely 1082 Research & Development
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## 322 322 34 No Travel_Rarely 1397 Research & Development
## 265 265 24 No Travel_Rarely 1127 Research & Development
## 150 150 28 No Travel_Rarely 580 Research & Development
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## 421 421 34 No Travel_Rarely 1153 Research & Development
## 231 231 45 No Travel_Rarely 1329 Research & Development
## 786 786 31 No Travel_Rarely 182 Research & Development
## 241 241 42 No Travel_Rarely 319 Research & Development
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## 99 99 29 No Travel_Frequently 410 Research & Development
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## 343 343 33 No Travel_Rarely 654 Research & Development
## 237 237 42 No Travel_Rarely 300 Research & Development
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## 68 68 23 No Travel_Rarely 571 Research & Development
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## 353 353 40 No Travel_Frequently 902 Research & Development
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## 492 492 50 No Travel_Rarely 1322 Research & Development
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## 384 384 23 No Travel_Rarely 541 Sales
## 644 644 34 Yes Non-Travel 967 Research & Development
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## 525 525 59 No Travel_Rarely 142 Research & Development
## 677 677 36 No Travel_Rarely 676 Research & Development
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## 825 825 36 No Travel_Rarely 559 Research & Development
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## 606 606 53 No Travel_Rarely 970 Research & Development
## 524 524 49 No Travel_Rarely 1245 Research & Development
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## 860 860 44 Yes Travel_Rarely 621 Research & Development
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## 82 82 35 No Travel_Rarely 1370 Research & Development
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## 477 477 37 No Travel_Rarely 342 Sales
## 314 314 51 No Travel_Rarely 1169 Research & Development
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## 36 36 32 No Travel_Frequently 1311 Research & Development
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## 788 788 52 Yes Travel_Rarely 723 Research & Development
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## 302 302 35 No Travel_Frequently 853 Sales
## 731 731 43 No Travel_Rarely 177 Research & Development
## 56 56 41 No Travel_Rarely 548 Research & Development
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## 437 437 28 Yes Travel_Rarely 890 Research & Development
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## 679 679 36 No Travel_Frequently 688 Research & Development
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## 247 247 31 No Travel_Rarely 1232 Research & Development
## 117 117 25 No Travel_Rarely 1356 Sales
## 845 845 34 No Travel_Rarely 511 Sales
## 364 364 40 No Travel_Rarely 1416 Research & Development
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## 180 180 38 No Travel_Rarely 702 Sales
## 338 338 34 No Travel_Frequently 618 Research & Development
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## 744 744 23 No Travel_Rarely 160 Research & Development
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## 400 400 44 No Travel_Frequently 1193 Research & Development
## 95 95 50 No Travel_Rarely 1452 Research & Development
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## 771 771 33 No Travel_Frequently 508 Sales
## 659 659 41 No Travel_Rarely 483 Research & Development
## 465 465 43 Yes Travel_Frequently 807 Research & Development
## 698 698 36 No Travel_Rarely 1396 Research & Development
## 798 798 22 Yes Travel_Rarely 1294 Research & Development
## 782 782 39 No Travel_Rarely 408 Research & Development
## 291 291 45 No Travel_Rarely 974 Research & Development
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## 211 211 41 No Travel_Rarely 582 Research & Development
## 634 634 36 No Travel_Rarely 922 Research & Development
## 654 654 48 No Travel_Rarely 530 Sales
## 775 775 42 No Travel_Frequently 288 Research & Development
## 559 559 42 No Travel_Rarely 933 Research & Development
## 805 805 44 No Non-Travel 489 Research & Development
## 178 178 26 Yes Travel_Frequently 342 Research & Development
## 110 110 32 No Travel_Rarely 529 Research & Development
## 857 857 30 Yes Travel_Rarely 1005 Research & Development
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## 115 115 35 No Travel_Rarely 1361 Sales
## 585 585 44 No Travel_Rarely 1459 Research & Development
## 460 460 36 No Travel_Rarely 172 Research & Development
## 505 505 47 No Travel_Rarely 359 Research & Development
## 576 576 37 No Travel_Rarely 1115 Research & Development
## 799 799 19 Yes Non-Travel 504 Research & Development
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## 233 233 47 Yes Travel_Frequently 1093 Sales
## 780 780 29 Yes Travel_Rarely 1092 Research & Development
## 129 129 43 No Travel_Rarely 930 Research & Development
## 837 837 37 No Travel_Rarely 1439 Research & Development
## 766 766 45 No Travel_Rarely 1015 Research & Development
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## 388 388 29 No Travel_Rarely 232 Research & Development
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## 308 308 34 No Non-Travel 697 Research & Development
## 829 829 38 No Travel_Frequently 594 Research & Development
## 94 94 35 No Travel_Rarely 670 Research & Development
## 289 289 36 No Travel_Frequently 1467 Sales
## 408 408 36 No Travel_Rarely 917 Research & Development
## 368 368 40 No Travel_Rarely 1342 Sales
## 164 164 42 No Travel_Rarely 916 Research & Development
## 413 413 37 No Travel_Rarely 408 Research & Development
## 487 487 30 Yes Travel_Rarely 945 Sales
## 767 767 41 No Travel_Rarely 509 Research & Development
## 733 733 27 Yes Travel_Frequently 1337 Human Resources
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## 808 808 22 No Travel_Rarely 1256 Research & Development
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## 552 552 28 No Travel_Rarely 1169 Human Resources
## 53 53 44 No Travel_Rarely 1117 Research & Development
## 409 409 53 No Travel_Rarely 1219 Sales
## 702 702 34 No Travel_Rarely 1400 Sales
## 280 280 55 No Travel_Rarely 1229 Research & Development
## 327 327 44 No Travel_Rarely 1313 Research & Development
## 415 415 34 Yes Travel_Frequently 296 Sales
## 83 83 38 No Travel_Frequently 508 Research & Development
## 396 396 31 No Travel_Rarely 192 Research & Development
## 133 133 42 No Travel_Frequently 1368 Research & Development
## 275 275 55 No Travel_Rarely 692 Research & Development
## 456 456 40 No Travel_Rarely 611 Sales
## 518 518 42 No Travel_Rarely 855 Research & Development
## 304 304 35 No Travel_Rarely 1395 Research & Development
## 238 238 32 No Travel_Rarely 646 Research & Development
## 507 507 33 Yes Travel_Rarely 350 Sales
## 482 482 31 No Travel_Rarely 1222 Research & Development
## 476 476 46 No Travel_Frequently 1211 Sales
## 434 434 29 No Travel_Rarely 590 Research & Development
## 495 495 36 No Travel_Rarely 1266 Sales
## 724 724 36 No Travel_Rarely 796 Research & Development
## 47 47 32 Yes Travel_Rarely 964 Sales
## 147 147 33 No Travel_Rarely 931 Research & Development
## 331 331 49 No Travel_Rarely 809 Research & Development
## 218 218 48 No Travel_Frequently 117 Research & Development
## 555 555 40 No Non-Travel 1151 Research & Development
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## 587 587 38 No Non-Travel 573 Research & Development
## 290 290 35 No Travel_Rarely 384 Sales
## 467 467 36 No Travel_Rarely 1157 Sales
## 393 393 31 No Non-Travel 976 Research & Development
## 255 255 40 No Non-Travel 218 Research & Development
## 558 558 34 No Travel_Rarely 254 Research & Development
## 300 300 28 Yes Travel_Rarely 1157 Research & Development
## 253 253 45 No Travel_Rarely 252 Research & Development
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## 59 59 37 No Non-Travel 728 Research & Development
## 811 811 30 No Non-Travel 111 Research & Development
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## 44 Single 19613 26362 8 Y
## 267 Divorced 17007 11929 7 Y
## 410 Married 17159 5200 6 Y
## 760 Single 18824 2493 2 Y
## 788 Married 4941 17747 2 Y
## 596 Divorced 4037 21816 1 Y
## 302 Married 9069 11031 1 Y
## 731 Divorced 4765 23814 4 Y
## 56 Divorced 2289 20520 1 Y
## 454 Single 5487 10410 1 Y
## 382 Married 2942 8916 1 Y
## 647 Single 1904 13556 1 Y
## 445 Divorced 19328 14218 7 Y
## 804 Single 9582 10333 0 Y
## 104 Married 2819 8544 2 Y
## 437 Single 4382 16374 6 Y
## 656 Married 4741 22722 1 Y
## 679 Divorced 5131 9192 7 Y
## 752 Single 2302 8319 1 Y
## 426 Divorced 10798 5268 5 Y
## 296 Married 2660 20232 7 Y
## 420 Married 11935 21526 1 Y
## 830 Married 18200 7999 1 Y
## 32 Married 2028 13637 1 Y
## 726 Married 4898 7505 0 Y
## 166 Single 2044 22052 1 Y
## 247 Single 7143 25713 1 Y
## 117 Single 4950 20623 0 Y
## 845 Single 6029 25353 5 Y
## 364 Divorced 19436 5949 0 Y
## 557 Single 3780 23428 7 Y
## 376 Single 14411 24450 1 Y
## 98 Divorced 5163 15850 5 Y
## 502 Single 2028 12947 5 Y
## 736 Single 6500 26997 0 Y
## 180 Single 8686 12930 4 Y
## 338 Single 7756 22266 0 Y
## 501 Married 10008 12023 7 Y
## 778 Divorced 2827 14947 1 Y
## 531 Married 8823 24608 0 Y
## 744 Single 3295 12862 1 Y
## 196 Married 2723 23231 1 Y
## 30 Married 4258 26589 0 Y
## 31 Divorced 4668 22812 0 Y
## 400 Single 10209 19719 5 Y
## 95 Single 19926 17053 3 Y
## 49 Single 1878 8059 1 Y
## 771 Single 4682 4317 3 Y
## 659 Single 6032 10110 6 Y
## 465 Married 2437 15587 9 Y
## 698 Single 5914 9945 8 Y
## 798 Married 2398 15999 1 Y
## 782 Single 4553 20978 1 Y
## 291 Divorced 2270 11005 3 Y
## 764 Married 6347 24920 0 Y
## 211 Married 13570 5640 0 Y
## 634 Divorced 2835 2561 5 Y
## 654 Married 12504 23978 3 Y
## 775 Married 10124 18611 2 Y
## 559 Married 4434 11806 1 Y
## 805 Married 2042 25043 4 Y
## 178 Married 2042 15346 6 Y
## 110 Single 2439 11288 1 Y
## 857 Single 2657 8556 5 Y
## 337 Divorced 4968 18500 1 Y
## 115 Married 8966 21026 3 Y
## 585 Married 6465 19121 2 Y
## 460 Single 5810 22604 1 Y
## 505 Married 17169 26703 3 Y
## 576 Divorced 5993 2689 1 Y
## 799 Single 1859 6148 1 Y
## 856 Married 2404 16192 1 Y
## 233 Married 12936 24164 7 Y
## 780 Married 4787 26124 9 Y
## 129 Single 4081 20003 1 Y
## 837 Married 2996 5182 7 Y
## 766 Single 5769 23447 1 Y
## 569 Single 6244 7824 7 Y
## 388 Divorced 4262 22645 4 Y
## 321 Married 2332 3974 6 Y
## 308 Married 2979 22478 3 Y
## 829 Married 2468 15963 4 Y
## 94 Single 6142 4223 3 Y
## 289 Married 9738 22952 0 Y
## 408 Divorced 2741 6865 1 Y
## 368 Married 5473 19345 0 Y
## 164 Single 6545 23016 3 Y
## 413 Married 3022 10227 4 Y
## 487 Single 1081 16019 1 Y
## 767 Married 3376 18863 1 Y
## 733 Married 2863 19555 1 Y
## 735 Single 2972 22061 1 Y
## 808 Married 2323 11992 1 Y
## 458 Single 5441 8423 0 Y
## 552 Divorced 4936 23965 1 Y
## 53 Married 2011 19982 1 Y
## 409 Married 15427 22021 2 Y
## 702 Married 5714 5829 1 Y
## 280 Married 4035 16143 0 Y
## 327 Divorced 19049 3549 0 Y
## 415 Divorced 2351 12253 0 Y
## 83 Married 5321 14284 2 Y
## 396 Divorced 2695 7747 0 Y
## 133 Married 4523 4386 0 Y
## 275 Single 18722 13339 8 Y
## 456 Single 19833 4349 1 Y
## 518 Divorced 2766 8952 8 Y
## 304 Single 5098 18698 1 Y
## 238 Married 6322 18089 1 Y
## 507 Single 2851 9150 1 Y
## 482 Married 2356 14871 3 Y
## 476 Single 5772 20445 4 Y
## 434 Divorced 2109 10007 1 Y
## 495 Married 5673 6060 1 Y
## 724 Single 8858 15669 0 Y
## 47 Single 6735 12147 6 Y
## 147 Married 2756 4673 1 Y
## 331 Single 15379 22384 4 Y
## 218 Divorced 17174 2437 3 Y
## 555 Married 4876 14242 9 Y
## 201 Single 13194 17071 4 Y
## 587 Divorced 5329 15717 7 Y
## 290 Married 2572 20317 1 Y
## 467 Single 2644 17001 3 Y
## 393 Divorced 3065 3995 1 Y
## 255 Divorced 13757 25178 2 Y
## 558 Married 3622 22794 1 Y
## 300 Married 3464 24737 5 Y
## 253 Single 2274 6153 1 Y
## 592 Single 4599 7815 0 Y
## 59 Divorced 13603 11677 2 Y
## 811 Divorced 3072 11012 1 Y
## 278 Single 2455 10675 0 Y
## 137 Married 2319 6689 1 Y
## 225 Divorced 4771 14293 2 Y
## 191 Married 5775 11934 1 Y
## 544 Married 6553 7259 9 Y
## 473 Married 13245 15067 4 Y
## 523 Divorced 9724 18787 2 Y
## 710 Divorced 17924 4544 1 Y
## 861 Single 2838 4257 0 Y
## 182 Divorced 6893 19461 3 Y
## 852 Single 2206 16117 1 Y
## 236 Married 10609 14922 5 Y
## 779 Single 2570 11925 1 Y
## 187 Divorced 5206 4973 1 Y
## 717 Married 6578 2706 1 Y
## 740 Single 3681 14004 4 Y
## 629 Married 10976 15813 3 Y
## 311 Married 5484 13008 9 Y
## 399 Divorced 6306 26236 1 Y
## 131 Divorced 4187 3356 1 Y
## 486 Single 5993 19479 8 Y
## 370 Single 5055 10557 7 Y
## 199 Married 10248 2094 3 Y
## 783 Divorced 2277 22650 3 Y
## 566 Single 2436 13422 6 Y
## 226 Married 3748 4077 1 Y
## 488 Divorced 2911 15170 1 Y
## 614 Single 2121 9947 1 Y
## 320 Single 2994 21221 1 Y
## 800 Divorced 6842 26308 6 Y
## 700 Married 19081 10849 5 Y
## 762 Married 16752 12982 1 Y
## 684 Divorced 9439 23402 3 Y
## 220 Single 2875 9973 1 Y
## 229 Married 4999 17519 0 Y
## 11 Married 19392 22539 7 Y
## 342 Married 3867 14222 1 Y
## 716 Married 1555 11585 1 Y
## 65 Married 2210 3372 1 Y
## 259 Married 9713 24444 2 Y
## 540 Single 5155 2253 7 Y
## 24 Single 5332 21602 7 Y
## 134 Married 5295 7693 4 Y
## 590 Married 4014 16002 3 Y
## 286 Married 5207 22949 1 Y
## 12 Divorced 2706 10494 1 Y
## 249 Divorced 5467 13953 3 Y
## 529 Single 2859 26278 1 Y
## 215 Single 2045 15174 0 Y
## 449 Single 2406 5456 1 Y
## 20 Married 5033 9364 2 Y
## 530 Married 4963 4510 9 Y
## 294 Married 11957 17231 0 Y
## 89 Single 8726 2975 1 Y
## 435 Divorced 9705 20652 2 Y
## 436 Married 6214 3415 1 Y
## 330 Married 16799 16616 0 Y
## 639 Married 2725 21630 1 Y
## 663 Divorced 10368 5596 4 Y
## 469 Single 4969 21813 8 Y
## 868 Married 4591 24200 3 Y
## 713 Single 2559 16620 5 Y
## 594 Married 3211 22102 1 Y
## 579 Single 5042 3140 0 Y
## 646 Married 10855 8552 7 Y
## 633 Single 6861 4981 8 Y
## 581 Married 3669 9075 3 Y
## 513 Married 3433 17360 6 Y
## 619 Married 9991 21457 4 Y
## 282 Single 4721 3119 2 Y
## 791 Married 4789 23070 4 Y
## 699 Single 1569 18420 1 Y
## 601 Married 2033 7103 1 Y
## 210 Divorced 8189 21196 3 Y
## 172 Married 5467 2125 8 Y
## 22 Married 8120 18597 3 Y
## 453 Single 5813 13492 1 Y
## 776 Single 3505 19630 1 Y
## 626 Divorced 2517 3208 1 Y
## 252 Divorced 5079 25952 4 Y
## 480 Married 13744 15471 1 Y
## 648 Married 2305 6217 1 Y
## 840 Married 19665 13583 4 Y
## 814 Married 19658 5220 3 Y
## 285 Married 2844 6004 1 Y
## 438 Single 2867 20006 0 Y
## 279 Single 5744 26959 1 Y
## 203 Married 6162 19124 1 Y
## 405 Single 2559 17852 1 Y
## 138 Married 10739 13943 8 Y
## 217 Married 9824 22174 3 Y
## 854 Single 5661 4824 0 Y
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 834 No 25 4 3
## 697 No 25 4 2
## 207 No 13 3 2
## 715 No 15 3 2
## 809 No 12 3 2
## 725 No 13 3 4
## 222 No 14 3 4
## 71 No 22 4 1
## 503 Yes 11 3 3
## 403 No 12 3 3
## 387 No 14 3 1
## 806 Yes 13 3 4
## 826 No 17 3 4
## 821 No 17 3 4
## 844 No 11 3 4
## 528 Yes 12 3 4
## 128 No 13 3 4
## 539 Yes 16 3 2
## 457 No 11 3 1
## 732 Yes 18 3 1
## 842 No 13 3 1
## 85 Yes 11 3 4
## 122 Yes 11 3 4
## 474 No 13 3 2
## 819 No 13 3 4
## 625 No 11 3 3
## 184 No 18 3 4
## 92 No 13 3 1
## 269 No 20 4 3
## 463 No 11 3 4
## 407 No 14 3 1
## 235 Yes 12 3 4
## 54 No 12 3 2
## 146 No 14 3 1
## 464 No 20 4 4
## 192 No 14 3 4
## 665 No 20 4 3
## 394 No 19 3 4
## 810 Yes 16 3 4
## 512 No 21 4 2
## 628 Yes 14 3 2
## 248 No 14 3 2
## 8 Yes 14 3 3
## 468 No 12 3 2
## 851 Yes 11 3 3
## 404 No 12 3 3
## 365 No 14 3 4
## 491 Yes 11 3 4
## 616 No 14 3 3
## 748 No 20 4 3
## 123 Yes 12 3 3
## 841 No 13 3 3
## 326 No 19 3 3
## 149 No 22 4 4
## 91 Yes 23 4 3
## 72 No 18 3 4
## 563 No 14 3 2
## 80 Yes 11 3 3
## 828 Yes 12 3 4
## 481 No 12 3 1
## 466 Yes 11 3 3
## 510 No 12 3 4
## 381 No 11 3 2
## 90 No 12 3 4
## 127 Yes 22 4 2
## 737 No 11 3 1
## 527 Yes 11 3 1
## 100 No 17 3 1
## 209 No 14 3 4
## 148 No 13 3 4
## 125 No 18 3 1
## 754 No 21 4 3
## 139 Yes 21 4 4
## 649 No 14 3 2
## 516 No 18 3 4
## 3 No 11 3 3
## 412 No 12 3 2
## 667 No 14 3 4
## 227 No 12 3 3
## 4 No 19 3 3
## 27 No 11 3 4
## 240 No 15 3 3
## 169 Yes 12 3 2
## 422 Yes 15 3 1
## 130 No 12 3 4
## 784 No 12 3 1
## 188 No 14 3 3
## 521 No 16 3 3
## 745 Yes 22 4 2
## 256 No 13 3 3
## 145 No 20 4 2
## 262 No 13 3 3
## 369 No 16 3 4
## 573 Yes 12 3 3
## 135 No 11 3 2
## 152 No 12 3 4
## 374 No 11 3 1
## 822 No 14 3 4
## 785 No 11 3 4
## 446 No 12 3 3
## 793 Yes 19 3 4
## 17 No 12 3 3
## 718 No 13 3 4
## 176 Yes 11 3 3
## 319 Yes 12 3 4
## 372 No 11 3 3
## 461 No 17 3 4
## 361 No 14 3 1
## 141 Yes 12 3 1
## 28 Yes 17 3 1
## 423 Yes 18 3 2
## 363 No 15 3 4
## 796 Yes 11 3 1
## 859 No 21 4 4
## 295 Yes 21 4 4
## 574 No 21 4 3
## 703 Yes 11 3 2
## 334 No 11 3 4
## 268 No 12 3 1
## 272 No 12 3 3
## 671 No 13 3 1
## 51 No 17 3 4
## 153 No 14 3 3
## 441 Yes 14 3 4
## 154 No 12 3 1
## 87 No 12 3 4
## 575 No 20 4 4
## 414 No 13 3 3
## 763 No 13 3 1
## 58 Yes 18 3 4
## 224 No 14 3 4
## 580 Yes 11 3 2
## 622 Yes 19 3 3
## 155 No 14 3 2
## 847 No 14 3 4
## 542 No 19 3 2
## 395 Yes 14 3 4
## 67 No 13 3 2
## 25 No 14 3 1
## 288 No 11 3 3
## 358 No 16 3 2
## 827 No 13 3 1
## 728 Yes 17 3 4
## 730 No 14 3 3
## 303 No 12 3 4
## 69 No 13 3 4
## 264 No 15 3 2
## 562 Yes 14 3 2
## 675 No 17 3 1
## 427 No 13 3 2
## 223 Yes 25 4 3
## 35 No 24 4 2
## 533 No 24 4 3
## 239 No 14 3 3
## 243 No 23 4 4
## 707 No 15 3 2
## 43 Yes 16 3 2
## 777 No 21 4 1
## 419 No 13 3 2
## 333 Yes 12 3 3
## 640 No 11 3 2
## 757 Yes 25 4 4
## 774 Yes 17 3 1
## 864 Yes 12 3 4
## 499 No 13 3 2
## 484 Yes 18 3 3
## 281 Yes 17 3 1
## 84 Yes 13 3 1
## 761 Yes 20 4 3
## 37 No 17 3 3
## 292 No 15 3 3
## 537 No 16 3 3
## 118 Yes 13 3 1
## 261 No 14 3 3
## 213 No 17 3 1
## 459 No 22 4 4
## 593 No 15 3 4
## 448 No 18 3 3
## 26 No 11 3 3
## 599 Yes 20 4 2
## 113 No 19 3 4
## 345 No 19 3 3
## 870 No 11 3 4
## 177 Yes 13 3 4
## 156 Yes 12 3 2
## 568 No 11 3 4
## 673 No 14 3 4
## 432 No 16 3 2
## 759 No 19 3 4
## 440 No 13 3 3
## 347 No 11 3 4
## 833 No 12 3 3
## 274 No 13 3 1
## 40 No 12 3 1
## 636 No 13 3 2
## 2 No 14 3 1
## 216 No 14 3 3
## 312 No 15 3 3
## 305 No 24 4 1
## 838 No 12 3 4
## 686 No 25 4 4
## 162 No 12 3 1
## 866 No 11 3 3
## 832 Yes 19 3 4
## 627 Yes 12 3 3
## 329 Yes 16 3 3
## 179 No 14 3 4
## 545 No 11 3 3
## 397 No 13 3 3
## 185 No 11 3 1
## 357 No 19 3 3
## 170 No 22 4 1
## 251 No 12 3 3
## 638 No 18 3 3
## 869 Yes 19 3 3
## 429 Yes 14 3 4
## 57 Yes 12 3 4
## 705 No 17 3 4
## 55 No 23 4 4
## 853 Yes 12 3 4
## 478 No 12 3 1
## 797 Yes 14 3 2
## 770 No 13 3 4
## 257 No 11 3 3
## 79 No 12 3 4
## 66 Yes 12 3 4
## 687 Yes 12 3 1
## 339 Yes 14 3 4
## 631 No 20 4 2
## 676 No 14 3 3
## 143 No 13 3 2
## 561 No 12 3 3
## 254 No 17 3 3
## 189 No 14 3 1
## 1 No 11 3 3
## 858 No 24 4 1
## 578 No 14 3 4
## 234 No 14 3 4
## 751 Yes 14 3 2
## 299 Yes 22 4 1
## 543 Yes 15 3 4
## 214 Yes 13 3 4
## 366 No 11 3 4
## 624 Yes 16 3 3
## 610 No 19 3 4
## 250 No 16 3 2
## 119 No 12 3 1
## 103 No 15 3 1
## 221 No 22 4 3
## 630 No 14 3 2
## 605 No 13 3 3
## 462 No 11 3 3
## 714 No 11 3 1
## 517 Yes 18 3 3
## 287 Yes 23 4 1
## 519 No 12 3 1
## 674 Yes 19 3 1
## 392 Yes 19 3 3
## 758 No 14 3 2
## 514 Yes 12 3 3
## 668 No 13 3 1
## 317 No 18 3 4
## 52 Yes 21 4 3
## 511 No 11 3 3
## 815 No 19 3 4
## 341 Yes 14 3 4
## 612 Yes 17 3 1
## 564 No 20 4 3
## 183 No 12 3 4
## 97 No 12 3 4
## 602 No 14 3 2
## 711 Yes 23 4 4
## 586 Yes 15 3 3
## 642 No 12 3 2
## 651 No 19 3 2
## 666 Yes 12 3 4
## 508 No 11 3 2
## 70 No 12 3 2
## 73 Yes 13 3 1
## 509 No 17 3 3
## 704 Yes 11 3 3
## 455 No 11 3 1
## 662 No 12 3 3
## 7 Yes 12 3 1
## 198 No 17 3 2
## 442 No 17 3 3
## 756 No 14 3 3
## 322 No 12 3 4
## 265 No 12 3 3
## 150 No 21 4 2
## 232 No 13 3 1
## 421 No 15 3 3
## 231 No 19 3 1
## 786 No 11 3 3
## 241 Yes 21 4 4
## 228 Yes 16 3 1
## 284 No 12 3 4
## 99 No 13 3 3
## 181 No 18 3 2
## 496 No 14 3 3
## 39 No 14 3 1
## 212 No 13 3 2
## 107 No 13 3 1
## 325 No 16 3 3
## 635 No 16 3 1
## 343 No 15 3 3
## 237 No 11 3 1
## 206 Yes 13 3 4
## 607 No 11 3 4
## 318 No 12 3 2
## 772 No 25 4 3
## 645 No 17 3 1
## 749 No 13 3 2
## 385 Yes 12 3 3
## 794 No 13 3 2
## 313 No 12 3 1
## 380 Yes 11 3 2
## 260 Yes 19 3 3
## 10 No 14 3 2
## 447 No 15 3 4
## 534 No 18 3 2
## 497 No 21 4 4
## 352 No 11 3 2
## 746 No 14 3 2
## 158 No 11 3 4
## 483 Yes 15 3 2
## 323 No 12 3 2
## 324 Yes 23 4 1
## 535 Yes 11 3 4
## 307 No 18 3 2
## 694 Yes 23 4 3
## 750 No 14 3 1
## 16 No 13 3 4
## 855 Yes 11 3 4
## 823 No 21 4 4
## 591 Yes 14 3 2
## 360 No 11 3 3
## 470 No 11 3 3
## 68 No 13 3 3
## 548 No 14 3 4
## 670 No 14 3 1
## 424 No 15 3 3
## 121 No 14 3 1
## 818 No 11 3 4
## 258 No 14 3 2
## 696 Yes 18 3 1
## 812 No 11 3 2
## 479 No 13 3 3
## 490 No 11 3 4
## 867 No 18 3 1
## 471 No 11 3 3
## 862 No 14 3 4
## 824 No 12 3 3
## 522 Yes 17 3 3
## 401 No 20 4 3
## 353 Yes 13 3 4
## 163 No 12 3 4
## 492 Yes 11 3 2
## 820 Yes 11 3 4
## 632 No 21 4 1
## 384 No 13 3 3
## 644 Yes 23 4 2
## 792 No 15 3 4
## 525 No 17 3 1
## 677 No 15 3 1
## 807 No 11 3 2
## 825 Yes 12 3 2
## 551 No 12 3 2
## 328 Yes 17 3 3
## 356 No 18 3 4
## 77 Yes 14 3 4
## 116 Yes 13 3 4
## 450 No 15 3 1
## 701 No 11 3 4
## 443 No 11 3 3
## 739 No 16 3 2
## 835 Yes 15 3 4
## 160 Yes 17 3 3
## 850 No 18 3 1
## 604 No 13 3 1
## 165 No 15 3 3
## 606 No 19 3 3
## 524 Yes 17 3 3
## 126 Yes 11 3 4
## 93 Yes 15 3 2
## 418 No 18 3 4
## 64 No 16 3 3
## 685 No 11 3 3
## 293 No 16 3 4
## 727 No 15 3 2
## 729 Yes 18 3 2
## 860 No 11 3 4
## 609 No 12 3 4
## 246 No 14 3 3
## 375 No 11 3 1
## 159 No 11 3 3
## 108 Yes 18 3 1
## 489 Yes 13 3 4
## 391 No 17 3 2
## 82 No 16 3 2
## 379 Yes 18 3 1
## 439 Yes 14 3 3
## 681 No 12 3 1
## 477 No 19 3 4
## 314 No 17 3 3
## 843 No 22 4 3
## 863 Yes 13 3 2
## 839 Yes 12 3 3
## 362 No 17 3 4
## 660 No 13 3 3
## 643 No 15 3 2
## 310 No 22 4 3
## 45 No 22 4 4
## 390 Yes 11 3 3
## 193 No 14 3 3
## 494 No 19 3 4
## 34 No 12 3 2
## 78 No 15 3 2
## 344 No 20 4 3
## 753 Yes 15 3 3
## 202 No 17 3 2
## 6 No 21 4 3
## 36 No 20 4 3
## 849 Yes 12 3 1
## 340 No 13 3 4
## 132 No 12 3 3
## 373 No 18 3 1
## 747 Yes 15 3 4
## 44 No 22 4 4
## 267 No 14 3 4
## 410 No 24 4 3
## 760 Yes 16 3 1
## 788 No 15 3 1
## 596 No 22 4 1
## 302 No 22 4 4
## 731 No 21 4 3
## 56 No 20 4 2
## 454 No 14 3 2
## 382 No 23 4 4
## 647 No 12 3 4
## 445 Yes 17 3 3
## 804 Yes 22 4 1
## 104 No 16 3 1
## 437 No 17 3 4
## 656 Yes 13 3 3
## 679 No 13 3 2
## 752 Yes 11 3 1
## 426 No 13 3 3
## 296 Yes 11 3 3
## 420 No 18 3 3
## 830 No 11 3 3
## 32 No 18 3 4
## 726 No 12 3 4
## 166 No 13 3 4
## 247 Yes 14 3 3
## 117 No 14 3 2
## 845 No 12 3 1
## 364 No 19 3 4
## 557 No 11 3 3
## 376 Yes 13 3 4
## 98 No 14 3 4
## 502 Yes 14 3 2
## 736 No 14 3 2
## 180 No 22 4 3
## 338 No 17 3 3
## 501 Yes 14 3 4
## 778 No 12 3 3
## 531 No 18 3 1
## 744 No 13 3 3
## 196 No 11 3 2
## 30 No 18 3 1
## 31 No 17 3 4
## 400 Yes 18 3 2
## 95 No 15 3 2
## 49 Yes 14 3 4
## 771 No 14 3 3
## 659 Yes 15 3 4
## 465 Yes 16 3 4
## 698 No 16 3 4
## 798 Yes 17 3 3
## 782 No 11 3 1
## 291 No 14 3 4
## 764 No 12 3 1
## 211 No 23 4 3
## 634 No 22 4 1
## 654 No 21 4 2
## 775 Yes 14 3 3
## 559 No 13 3 4
## 805 No 12 3 3
## 178 Yes 14 3 2
## 110 No 14 3 4
## 857 Yes 11 3 3
## 337 No 11 3 4
## 115 Yes 15 3 4
## 585 Yes 13 3 4
## 460 No 16 3 3
## 505 No 19 3 2
## 576 No 18 3 3
## 799 Yes 25 4 2
## 856 No 13 3 1
## 233 No 11 3 3
## 780 Yes 14 3 2
## 129 Yes 14 3 1
## 837 Yes 15 3 4
## 766 Yes 14 3 1
## 569 No 17 3 1
## 388 No 12 3 2
## 321 No 20 4 3
## 308 No 17 3 4
## 829 No 14 3 2
## 94 Yes 16 3 3
## 289 No 14 3 3
## 408 No 14 3 3
## 368 No 12 3 4
## 164 Yes 13 3 3
## 413 No 21 4 1
## 487 No 13 3 3
## 767 No 13 3 3
## 733 No 12 3 1
## 735 No 13 3 3
## 808 No 24 4 1
## 458 Yes 22 4 4
## 552 No 13 3 4
## 53 No 13 3 4
## 409 No 16 3 3
## 702 No 20 4 1
## 280 Yes 16 3 2
## 327 Yes 14 3 4
## 415 No 16 3 4
## 83 No 11 3 4
## 396 Yes 18 3 2
## 133 No 11 3 4
## 275 No 11 3 4
## 456 No 14 3 2
## 518 No 22 4 2
## 304 No 19 3 2
## 238 Yes 12 3 4
## 507 Yes 13 3 2
## 482 Yes 19 3 2
## 476 Yes 21 4 3
## 434 No 13 3 3
## 495 Yes 13 3 1
## 724 No 11 3 2
## 47 No 15 3 2
## 147 No 13 3 4
## 331 No 14 3 1
## 218 No 11 3 2
## 555 No 14 3 4
## 201 Yes 16 3 4
## 587 Yes 12 3 4
## 290 No 16 3 2
## 467 Yes 21 4 4
## 393 Yes 13 3 4
## 255 No 11 3 3
## 558 Yes 13 3 4
## 300 Yes 13 3 4
## 253 No 14 3 4
## 592 Yes 23 4 3
## 59 Yes 18 3 1
## 811 No 11 3 3
## 278 No 19 3 1
## 137 Yes 11 3 4
## 225 No 19 3 4
## 191 No 13 3 4
## 544 No 14 3 2
## 473 Yes 14 3 2
## 523 No 17 3 3
## 710 No 11 3 4
## 861 No 14 3 2
## 182 No 15 3 4
## 852 No 13 3 1
## 236 No 11 3 3
## 779 No 20 4 3
## 187 No 17 3 3
## 717 No 18 3 1
## 740 No 14 3 4
## 629 No 18 3 2
## 311 No 17 3 2
## 399 No 21 4 1
## 131 Yes 13 3 2
## 486 Yes 11 3 1
## 370 No 16 3 3
## 199 No 14 3 4
## 783 Yes 11 3 3
## 566 Yes 12 3 3
## 226 No 13 3 3
## 488 No 17 3 4
## 614 Yes 13 3 2
## 320 Yes 12 3 4
## 800 No 20 4 1
## 700 No 11 3 1
## 762 Yes 11 3 3
## 684 Yes 16 3 2
## 220 Yes 20 4 2
## 229 No 21 4 1
## 11 No 13 3 4
## 342 Yes 12 3 2
## 716 No 11 3 3
## 65 No 13 3 1
## 259 Yes 13 3 4
## 540 No 13 3 4
## 24 No 13 3 4
## 134 No 21 4 3
## 590 Yes 15 3 3
## 286 Yes 12 3 2
## 12 No 15 3 2
## 249 Yes 14 3 1
## 529 No 18 3 1
## 215 No 13 3 4
## 449 No 11 3 4
## 20 No 15 3 4
## 530 Yes 18 3 1
## 294 No 18 3 1
## 89 No 15 3 4
## 435 No 12 3 2
## 436 No 18 3 1
## 330 No 14 3 3
## 639 Yes 11 3 2
## 663 Yes 12 3 2
## 469 No 18 3 4
## 868 Yes 17 3 3
## 713 No 11 3 3
## 594 No 14 3 4
## 579 No 13 3 4
## 646 No 11 3 1
## 633 Yes 12 3 3
## 581 No 11 3 3
## 513 No 13 3 1
## 619 No 15 3 1
## 282 Yes 13 3 3
## 791 No 25 4 1
## 699 Yes 12 3 3
## 601 No 13 3 3
## 210 Yes 13 3 3
## 172 No 18 3 3
## 22 No 12 3 4
## 453 Yes 18 3 4
## 776 No 18 3 4
## 626 No 11 3 2
## 252 No 13 3 4
## 480 Yes 25 4 1
## 648 No 15 3 3
## 840 No 12 3 4
## 814 No 11 3 3
## 285 No 13 3 4
## 438 No 13 3 4
## 279 Yes 11 3 4
## 203 No 12 3 3
## 405 No 11 3 4
## 138 No 11 3 3
## 217 No 19 3 3
## 854 No 19 3 3
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
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## 854 0 7ddsTest = DDSAnalytics[-ddsObs,]
DDSAnalytics$YearsAtCompany_log = log(DDSAnalytics$YearsAtCompany)
DDSAnalytics$MonthlyRate_log = log(DDSAnalytics$MonthlyRate)
DDSAnalytics$NumCompaniesWorked_log = log(DDSAnalytics$NumCompaniesWorked) # many -inf no use
# fitting or training our model on Training Set
ddsModel <- lm(MonthlyIncome ~ Age + NumCompaniesWorked + JobSatisfaction+JobLevel+
YearsAtCompany +TotalWorkingYears, data = ddsTrain) # train data model
summary(ddsModel)##
## Call:
## lm(formula = MonthlyIncome ~ Age + NumCompaniesWorked + JobSatisfaction +
## JobLevel + YearsAtCompany + TotalWorkingYears, data = ddsTrain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5709.8 -841.0 58.5 719.4 3827.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1624.093 291.344 -5.574 3.65e-08 ***
## Age -3.971 8.175 -0.486 0.62728
## NumCompaniesWorked -18.279 23.638 -0.773 0.43964
## JobSatisfaction 6.326 47.176 0.134 0.89337
## JobLevel 3665.519 77.093 47.547 < 2e-16 ***
## YearsAtCompany -40.092 13.134 -3.053 0.00236 **
## TotalWorkingYears 91.067 16.487 5.524 4.82e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1340 on 645 degrees of freedom
## Multiple R-squared: 0.9191, Adjusted R-squared: 0.9184
## F-statistic: 1221 on 6 and 645 DF, p-value: < 2.2e-16dfres <- data.frame(ddsModel$residuals)
dfres%>%ggplot(aes(ddsModel.residuals,fill="#DC143C"))+geom_histogram(color="white")+ggtitle("Residual Histogram")+xlab("Residuals")+
theme(panel.background = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#hist(ddsModel$residuals,col="#1E90FF",main = "Residual Histogram")
plot(ddsModel$residuals,col="#F8766D",main = "Residual Plots")
library(ggpubr)
ggqqplot(ddsModel$residuals)
# #1E90FF", "#DC143C
# Testing our model built using train set with the test data set
ddsTest$MonthlyIncomePred = predict(ddsModel, newdata = ddsTest)
# MS_PE = data.frame(Observed = ddsTest$MonthlyIncome, Predicted = ddsTest$MonthlyIncomePred)
# # calculating MSPE to see how good our model is.
# MS_PE$Residual = MS_PE$Observed - MS_PE$Predicted
# MS_PE$SquaredResidual = MS_PE$Residual^2
#
# mean(MS_PE$SquaredResidual)
# RMSE(MS_PE$Observed,MS_PE$Predicted)Monthly Income Predicton
# I have also provided an additional data set of 300 observations that do not have the Monthly Incomes. This data is in the file “CaseStudy2CompSet No Salary.csv”.
# Importing No Salary data to predict the salary
DDSAnalyticsNoSalary <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Salary.csv",sep = ",",header = TRUE)
# Use model to predict the unkown Montly Income from dataset.
ddsNoSalPreds = predict(ddsModel,newdata = DDSAnalyticsNoSalary)
as.data.frame(ddsNoSalPreds)->ddsAnalyticSalPreds
names(ddsAnalyticSalPreds) <- c("Predicted Salary")
head(ddsAnalyticSalPreds)## Predicted Salary
## 1 5900.026
## 2 2215.897
## 3 14152.723
## 4 2147.704
## 5 2279.071
## 6 7212.705DDSAnalyticsNoSalary$PredictedMonthlyIncome = ddsNoSalPreds #add PredictedSalary col and put the predicted values into tht column.
library(dplyr)
DDSAnalyticsNoSalary%>%dplyr::select(ID,PredictedMonthlyIncome)->PredictionWithID
head(PredictionWithID) # dataframe with ID and PredictedMonthlyIncome. ## ID PredictedMonthlyIncome
## 1 871 5900.026
## 2 872 2215.897
## 3 873 14152.723
## 4 874 2147.704
## 5 875 2279.071
## 6 876 7212.705write.csv(PredictionWithID,"DDSIncomePrediction.csv") # write csv file to ExcelTurnover Analysis
#Turnover Analysis
#https://corpgov.law.harvard.edu/2018/01/22/the-option-to-quit-the-effect-of-employee-stock-options-on-turnover/
library(plyr)## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following object is masked from 'package:ggpubr':
##
## mutate## The following object is masked from 'package:maps':
##
## ozone## The following object is masked from 'package:purrr':
##
## compact## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarizelibrary(dplyr)
library(tidyverse)
# Donut chart Attrition Percentage
Att <- DDSAnalytics%>%group_by(Attrition)%>%
dplyr::summarize(count=n(),
percentage = n()/nrow(DDSAnalytics))
Att## # A tibble: 2 x 3
## Attrition count percentage
## <fct> <int> <dbl>
## 1 No 730 0.839
## 2 Yes 140 0.161Attdonut <- ggplot(data = Att,aes(x=2,y=percentage,fill=Attrition))+
geom_col(color="white")+
coord_polar("y", start = 1)+
geom_text(aes(label=paste0(round(percentage*100),"%")),
position = position_stack(vjust = 0.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Attrition Percentage")+
xlim(0.5,2.5)
Attdonut
# Donut chart Marital Sttus percentage
ddsMaritalStat <- DDSAnalytics%>%group_by(MaritalStatus)%>%
dplyr::summarize(count=n(),
Mpercentage=n()/nrow(DDSAnalytics))
ddsMaritalStatDonut <- ggplot(data = ddsMaritalStat,aes(x=2,y=Mpercentage,fill=MaritalStatus))+
geom_col(color="white")+
coord_polar("y",start=1)+
geom_text(aes(label=paste0(round(Mpercentage*100),"%")),
position = position_stack(vjust = 0.5),color="white",size=I(4.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Marital Status Percentage")+
xlim(0.3,2.5)
ddsMaritalStatDonut
DDSAnalytics$StockOptionLevel = as.factor(DDSAnalytics$StockOptionLevel)
DDSAnalytics$JobInvolvement = as.factor(DDSAnalytics$JobInvolvement)
#JobInvolvement Donut chart percentage
ddsJobInv <- DDSAnalytics%>%group_by(JobInvolvement)%>%
dplyr::summarize(count=n(),
InvPercentage = n()/nrow(DDSAnalytics))
ddsJobInvDonut <- ggplot(data = ddsJobInv, aes(x=2,y=InvPercentage,fill=JobInvolvement))+
geom_col(color="white")+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(InvPercentage*100),"%")),
position=position_stack(vjust = 0.5),size=I(5.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Job Involvement Percentage")+
xlim(0.5,2.5)
ddsJobInvDonut 
# Attrition based on Job Involvement bar chart
DDSAnalytics%>%ggplot(aes(x=JobInvolvement,fill=Attrition))+geom_bar()+
ggtitle("Involvement Keeps Employees In The Company")+
xlab("Job Involvement")+ylab("Employee Count")+
theme(panel.background = element_blank(),
plot.title = element_text(hjust = 0.5,size=I(18)),
axis.text = element_text(size = I(12)),
axis.title = element_text(size=I(15)))
#panel.background = element_rect(fill = "lightblue",
# colour = "lightblue",
# Stock Option Level pie chart
ddsStockOpt <- DDSAnalytics%>%group_by(StockOptionLevel)%>%
dplyr::summarize(count=n(),
StockPercentage = n()/nrow(DDSAnalytics))
ddsStockPie <- ggplot(data=ddsStockOpt,aes(x=2,y=StockPercentage,fill=StockOptionLevel))+
geom_col(color="white")+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(StockPercentage*100),"%")),
position = position_stack(vjust = 0.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
plot.title = element_text(hjust = 0.5,size = I(19)))+
ggtitle("Percent Of Employees Owning Stock Option")
ddsStockPie
# Bar Chart Marital Status and Attrition Relationship
DDSAnalytics%>%ggplot(aes(MaritalStatus))+geom_bar(aes(fill=Attrition))+
ggtitle("Higher Attrition Among Sinlge Employee")+xlab("Marital Status")+
theme(plot.title = element_text(hjust = 0.5,size = I(17)))->AttvsMart
AttvsMart
figure <- ggarrange(Attdonut,ddsMaritalStatDonut,AttvsMart,
labels = c("A", "B","C"),
ncol = 3, nrow = 1)
figure
# Donut chart Relationship StockOptionLevel Attrition 'Yes'
DDSAnalytics%>%filter(Attrition=="Yes")->AttYes
AttYes%>%ggplot(aes(Attrition))+geom_bar(aes(fill=StockOptionLevel))+
ggtitle("Employe Attrition Based On Stock Option ")+
theme(plot.title = element_text(hjust = 0.5,size = I(20)),
axis.text = element_text(size=I(17)),
axis.title = element_text(size=I(18)),
panel.background = element_blank(),
axis.title.y = element_blank()
)
DDSAnalytics%>%ggplot(aes(StockOptionLevel))+geom_bar(aes(fill=MaritalStatus))+
ggtitle("Not Enough Stock Option For Single Employee ")+
theme(plot.title = element_text(hjust = 0.5,size = I(20)),
axis.text = element_text(size=I(17)),
axis.title = element_text(size=I(18)),
panel.background = element_blank(),
axis.title.y = element_blank()
)
library(dplyr)
DDSAnalytics%>%ggplot(aes(JobInvolvement, fill= Attrition))+
geom_bar() +
ggtitle("Age Group Attrition ")+
theme(plot.title = element_text(hjust = 0.5,size = I(20)),
axis.text = element_text(size=I(17)),
axis.title = element_text(size=I(18)),
panel.background = element_blank()
)
DDSAnalytics%>%ggplot(aes(x=JobRole,fill=Attrition))+
geom_bar()
DDSAnalytics%>%ggplot(aes(y=MonthlyIncome,x=Attrition,fill=Attrition))+
geom_boxplot()+ggtitle("Lower Income Cause Of Attrition")+
xlab("Attrition")+ylab("Monthly Income")+
theme(panel.background =element_rect(fill = "white", colour = "grey50"),
plot.title = element_text(hjust = 0.5,size=I(18)),
axis.text = element_text(size=I(13)),
axis.title = element_text(size=I(15)))
#Lower Monthlty Cause Of Attrition
DDSAnalytics%>%ggplot(aes(y=MonthlyRate,x=Attrition,fill=Attrition))+
geom_boxplot()+ggtitle("Lower Monthlty Rate Cause Of Attrition")+
xlab("Attrition")+ylab("Monthly Rate")+
theme(panel.background =element_rect(fill = "white", colour = "grey50"),
plot.title = element_text(hjust = 0.5,size=I(18)),
axis.text = element_text(size=I(13)),
axis.title = element_text(size=I(15)),
legend.position = "none")
# Monthly Income Attrition Jobrole boxplot
DDSAnalytics%>%ggplot(aes(y=MonthlyIncome,x=Attrition,fill=JobRole))+geom_boxplot()+
ggtitle("Job Role Income Analysis")+
xlab("Attrition")+ylab("Monthly Income")+
theme(panel.grid.major = element_line(color="grey70",size=0.2),
panel.background = element_rect(fill = "white", colour = "grey50"),
plot.title = element_text(hjust = 0.5,size=I(18)),
axis.text = element_text(size=I(13)),
axis.title = element_text(size=I(15))
)
#MonthlyIncome,Attrition,JobRole ggpairs
DDSAnalytics%>%dplyr::select(MonthlyIncome,Attrition,JobRole)%>%
ggpairs(aes(color=JobRole))+
ggtitle("Job Role Income Analysis Based On Attrition")+
xlab("Attrition")+ylab("Monthly Income")+
theme(panel.grid.major = element_line(color="grey70",size=0.2),
panel.background = element_rect(fill = "white", colour = "grey50"),
plot.title = element_text(hjust = 0.5,size=I(24)),
axis.text = element_text(size=I(13)),
axis.title = element_text(size=I(15))
)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Subseting data set based on Attrtion YES and NO
DDSAnalytics%>%ggplot(aes(OverTime))+geom_bar(aes(fill=Attrition))
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=YearsSinceLastPromotion))+geom_point(aes(color=Attrition))
DDSAnalytics%>%ggplot(aes(Gender))+geom_bar(aes(fill=Attrition))
DDSAnalytics%>%filter(Attrition=="Yes")->AttYes
DDSAnalytics%>%filter(Attrition=="No")->AttNo
library(dplyr)
#Job Involvement Rating Percentage Attrition YES
AttYes$JobInvolvement = as.factor(AttYes$JobInvolvement)
AttYesJobInvCt <- AttYes%>%group_by(JobInvolvement)%>%
dplyr::summarize(count=n(),
JobInvPercent = n()/nrow(AttYes))
jobInvYesAtt <- ggplot(data = AttYesJobInvCt,aes(x=2,y=JobInvPercent,fill=JobInvolvement))+
geom_col(color="white")+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(JobInvPercent*100),"%")),
position = position_stack(vjust = 0.5),size=I(5.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "none",
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Job Involvement Rating Percentage Attrition \nYes")+
xlim(0.5,2.5)
jobInvYesAtt 
#Job Involvement Rating Percentage Attrition NO
AttNo$JobInvolvement = as.factor(AttNo$JobInvolvement)
AttNoJobInvCt <- AttNo%>%group_by(JobInvolvement)%>%
dplyr::summarize(count=n(),
JobInvPercentAttNo = n()/nrow(AttNo))
jobInvNoAttDonut <- ggplot(data = AttNoJobInvCt,aes(x=2,y=JobInvPercentAttNo,fill=JobInvolvement))+
geom_col(color="white")+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(JobInvPercentAttNo*100),"%")),
position = position_stack(vjust = 0.5),size=I(4.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "none",
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Job Involvement Rating Percentage Attrition \nNo")+
xlim(0.5,2.5)
jobInvNoAttDonut 
# Attrition Yes Over Time Analysis
AttYesOvrtm <- AttYes%>%group_by(OverTime)%>%
dplyr::summarize(count=n(),
OverTmPercent = n()/nrow(AttYes))
attOverTime <- ggplot(data = AttYesOvrtm,aes(x=3,y=OverTmPercent,fill=OverTime))+
geom_col(color="white")+
scale_fill_manual(values=c("#1E90FF", "#DC143C"))+
coord_polar("y",start=1)+
geom_text(aes(label=paste0(round(OverTmPercent*100),"%")),
position = position_stack(vjust = 0.5),size=I(6),color="white")+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.text = element_text(size = I(13)),
plot.title = element_text(hjust = 0.5,size=I(19))
)+
ggtitle("Over Time And Attrition \n YES")+
xlim(0.1,3.5)
attOverTime 
# N0 Attrition vs Over Time
AttNoOvtm <- AttNo%>%group_by(OverTime)%>%
dplyr::summarize(count=n(),
OverTmPercentNatt = n()/nrow(AttNo))
attOverTimeNatt <- ggplot(data = AttNoOvtm,aes(x=3,y=OverTmPercentNatt,fill=OverTime))+
geom_col(color="white")+
scale_fill_manual(values = c("#1E90FF", "#DC143C"))+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(OverTmPercentNatt*100),"%")),
position = position_stack(vjust = 0.5),size=I(6),color="white")+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(19)))+
ggtitle("Over Time And Attrition \n NO")+
xlim(0.1,3.5)
attOverTimeNatt
#Q. 4. The business is also interested in learning about any job role specific trends that may exist in the data set (e.g., “Data Scientists have the highest job satisfaction”).
as.factor(DDSAnalytics$JobSatisfaction)->jobsatFac
str(jobsatFac)## Factor w/ 4 levels "1","2","3","4": 4 3 4 4 4 1 3 4 3 3 ...dfjobSat <- data.frame(jRol=DDSAnalytics$JobRole,Satisfaction=jobsatFac)
# ordering bars in ascending order
dfjobSat <- within(dfjobSat,
jRol <- factor(jRol,levels = names(sort(table(jRol),decreasing = FALSE))))
ggplot(dfjobSat,aes(x=jRol))+geom_bar(aes(fill=Satisfaction))+ggtitle("Job Role Vs Job Satisfaction")+
xlab("Job Role")+ylab("Number of Employee ")+coord_flip()+theme(plot.title = element_text(
color="Black",
hjust = 0.5,
size = 18,
),axis.text.y = element_text(color="Black",
size = 15),
axis.text.x = element_text(color="black",
size=15),
axis.title.x = element_text(size = 17),
axis.title.y = element_text(size = 17),
panel.background = element_blank()
)-> df2
df2
##Q.5. You can also provide any other interesting trends and observations from your analysis.
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=Age,color=JobRole))+
geom_point(aes(size=I(2.3)))+ggtitle("Scatter Plot Of Job Role Based On Age And Years At The Company")+
labs(x="Years at the company",y="Employee Age")+theme(
plot.title = element_text(color = "#000000",
size = 12,
hjust = 0.5),
axis.text = element_text(color = "#000000",
size = 14),
axis.title = element_text(color = "#000000",
size=16),
legend.text = element_text(color = "#000000",
size = 13),
panel.background = element_blank()
)
Attrition Classifcation Section
#DDSAnalyticsNoAttrition <-read.csv(file.choose())
DDSAnalyticsNoAttritionknn <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv",sep=",",header = TRUE)
DDSAnalyticsCfknn <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)
DDSAnalyticsCfknn%>%dplyr::select(MonthlyIncome, Age, NumCompaniesWorked,JobSatisfaction,JobLevel,YearsAtCompany,
TotalWorkingYears)->cl
clCor <- cor(cl)
corrplot(clCor,method = "number")
DDSAnalyticsCfknn$OverTime = as.numeric(DDSAnalyticsCfknn$OverTime)
DDSAnalyticsCfknn$Gender = as.numeric(DDSAnalyticsCfknn$Gender)
DDSAnalyticsCfknn$BusinessTravel= as.numeric(DDSAnalyticsCfknn$BusinessTravel)
DDSAnalyticsCfknn$MaritalStatus = as.numeric(DDSAnalyticsCfknn$MaritalStatus)
DDSAnalyticsCfknn$JobRole = as.numeric(DDSAnalyticsCfknn$JobRole)
DDSAnalyticsCfknn$MonthlyIncome_log = log(DDSAnalyticsCfknn$MonthlyIncome)
DDSAnalyticsCfknn$MonthlyRate_log = log(DDSAnalyticsCfknn$MonthlyRate)kNN Classification
DDSAnalyticsCfknn$OverTime <- ifelse(DDSAnalyticsCfknn$OverTime=="yes",1,0)
mstatus <- as.data.frame(dummy.code(DDSAnalyticsCfknn$MaritalStatus))
jRol <- as.data.frame(dummy.code(DDSAnalyticsCfknn$JobRole))
knnclsDataframe <- data.frame(Attrition=DDSAnalyticsCfknn$Attrition,
JobInvolvement=DDSAnalyticsCfknn$JobInvolvement,
JobRole=jRol,
MaritalStatus = mstatus,
OverTime = DDSAnalyticsCfknn$OverTime,
StockOptionLevel= DDSAnalyticsCfknn$StockOptionLevel,
JobLevel=DDSAnalyticsCfknn$JobLevel,
JobSatisfaction=DDSAnalyticsCfknn$JobSatisfaction,
YearsAtCompany =DDSAnalyticsCfknn$YearsAtCompany,
YearsInCurrentRole=DDSAnalyticsCfknn$YearsInCurrentRole)
DDSAnalyticsNoAttritionknn$OverTime <- ifelse(DDSAnalyticsNoAttritionknn$OverTime=="yes",1,0)
mstatus <- as.data.frame(dummy.code(DDSAnalyticsNoAttritionknn$MaritalStatus))
jRol <- as.data.frame(dummy.code(DDSAnalyticsNoAttritionknn$JobRole))
knnPredictionDataframe <- data.frame(JobInvolvement = DDSAnalyticsNoAttritionknn$JobInvolvement,
JobRole = jRol,
MaritalStatus = mstatus,
OverTime = DDSAnalyticsNoAttritionknn$OverTime,
StockOptionLevel = DDSAnalyticsNoAttritionknn$StockOptionLevel,
JobLevel = DDSAnalyticsNoAttritionknn$JobLevel,
JobSatisfacton= DDSAnalyticsNoAttritionknn$JobSatisfaction,
yearAtCompany = DDSAnalyticsNoAttritionknn$YearsAtCompany)#kNN Classification cross validation
# spliting data set knnclsDataframe
set.seed(7)
ddsObsknn = sample(seq(1,dim(knnclsDataframe)[1]),round(.75*dim(knnclsDataframe)[1]),replace = FALSE)
ddsknnTrain = knnclsDataframe[ddsObsknn,]
ddsknnTest = knnclsDataframe[-ddsObsknn,]
# kNN crossvalidation
AttClassification=knn(ddsknnTrain[,2:19],ddsknnTest[,2:19],ddsknnTrain$Attrition,prob = TRUE,k=17)
table(AttClassification,ddsknnTest$Attrition)##
## AttClassification No Yes
## No 184 21
## Yes 5 8caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))## Confusion Matrix and Statistics
##
##
## AttClassification No Yes
## No 184 21
## Yes 5 8
##
## Accuracy : 0.8807
## 95% CI : (0.8301, 0.9206)
## No Information Rate : 0.867
## P-Value [Acc > NIR] : 0.315662
##
## Kappa : 0.3254
##
## Mcnemar's Test P-Value : 0.003264
##
## Sensitivity : 0.9735
## Specificity : 0.2759
## Pos Pred Value : 0.8976
## Neg Pred Value : 0.6154
## Prevalence : 0.8670
## Detection Rate : 0.8440
## Detection Prevalence : 0.9404
## Balanced Accuracy : 0.6247
##
## 'Positive' Class : No
## kCM = caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))
kCM## Confusion Matrix and Statistics
##
##
## AttClassification No Yes
## No 184 21
## Yes 5 8
##
## Accuracy : 0.8807
## 95% CI : (0.8301, 0.9206)
## No Information Rate : 0.867
## P-Value [Acc > NIR] : 0.315662
##
## Kappa : 0.3254
##
## Mcnemar's Test P-Value : 0.003264
##
## Sensitivity : 0.9735
## Specificity : 0.2759
## Pos Pred Value : 0.8976
## Neg Pred Value : 0.6154
## Prevalence : 0.8670
## Detection Rate : 0.8440
## Detection Prevalence : 0.9404
## Balanced Accuracy : 0.6247
##
## 'Positive' Class : No
## Finding Mean Accuracy knn
set.seed(1)
iterations = 100
numks = 60
splitPerc = .95
masterAcc = matrix(nrow = iterations, ncol = numks)
for(j in 1:iterations)
{
ddsObsknn = sample(seq(1,dim(knnclsDataframe)[1]),round(.75*dim(knnclsDataframe)[1]),replace = FALSE)
ddsknnTrain = knnclsDataframe[ddsObsknn,]
ddsknnTest = knnclsDataframe[-ddsObsknn,]
for(i in 1:numks)
{
AttClassification=knn(ddsknnTrain[,2:19],ddsknnTest[,2:19],ddsknnTrain$Attrition,prob = TRUE,k=i)
table(AttClassification,ddsknnTest$Attrition)
kCM = caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))
masterAcc[j,i] = kCM$overall[1]
}
}
MeanAcc = colMeans(masterAcc)
plot(seq(1,numks,1),MeanAcc, type = "l")
which.max(MeanAcc)## [1] 18max(MeanAcc)## [1] 0.8431193# # normalizing the values because Knn does the classification based on distances and it does better job when all the variables are normalized and are in same scalling.
# normalize <- function(x){
# return((x-min(x))/(max(x)-min(x)))
# }
# #for example normalize(c(1,2,3,4,5))
# # for exampl normalize(c(20,30,40,50,100))
# # lets apply the function to the variables we want to normalize
# # e.g: iris_n <- as.data.frame (lapply(iris[,c(1,2,3,4)],normalize))
#
# DDSAnalytics_normalized = as.data.frame(lapply(DDSAnalyticsCf[c(18,32,35,36,22,29,15,7)],normalize))
# summary(DDSAnalytics_normalized)
# dataFrame1 <- data.frame(
# YearsSinceLastPromotion = DDSAnalytics_normalized$YearsSinceLastPromotion,
# StockOptionLevel = DDSAnalytics_normalized$StockOptionLevel,
# NumCompaniesWorked = DDSAnalytics_normalized$NumCompaniesWorked,
# JobSatisfaction= DDSAnalytics_normalized$JobSatisfaction,
# JobInvolvement = DDSAnalytics_normalized$JobInvolvement,
# DistanceFromHome = DDSAnalytics_normalized$DistanceFromHome,
# Attrition = DDSAnalyticsCf$Attrition,
# Gender = DDSAnalyticsCf$Gender,
# MaritalStatus = DDSAnalyticsCf$MaritalStatus,
# BusinessTravelTravel= DDSAnalyticsCf$BusinessTravel)
#
# library(class)
# library(caret)# DDSAnalyticsCf$OverTime = as.numeric(DDSAnalyticsCf$OverTime)
# DDSAnalyticsCf$Gender = as.numeric(DDSAnalyticsCf$Gender)
# DDSAnalyticsCf$BusinessTravel= as.numeric(DDSAnalyticsCf$BusinessTravel)
# DDSAnalyticsCf$MaritalStatus = as.numeric(DDSAnalyticsCf$MaritalStatus)
# DDSAnalyticsCf$JobRole = as.numeric(DDSAnalyticsCf$JobRole)
#
# lotistic <- glm(Attrition ~ YearsWithCurrManager + Age +YearsInCurrentRole+ YearsSinceLastPromotion + TotalWorkingYears + TrainingTimesLastYear+ WorkLifeBalance + YearsAtCompany + StockOptionLevel+PercentSalaryHike+ PerformanceRating+RelationshipSatisfaction+ NumCompaniesWorked+MonthlyIncome+ MonthlyRate+JobSatisfaction+JobInvolvement+HourlyRate+EnvironmentSatisfaction+DistanceFromHome+DailyRate+Gender+
# +JobLevel+ Education+MaritalStatus+EducationField+BusinessTravel+Department+JobRole, data = DDSAnalyticsCf, family = "binomial")
# summary(lotistic)NaiveBayes Classification
DDSAnalyticsNoAttrition <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv", sep=",",header = TRUE)
DDSAnalyticsCf <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)# using naive Bayes on original data
set.seed(7)
ddsObs = sample(seq(1,dim(DDSAnalyticsCf)[1]),round(.75*dim(DDSAnalyticsCf)[1]),replace = FALSE)
ddsTrain = DDSAnalyticsCf[ddsObs,]
dim(ddsTrain)## [1] 652 36ddsTest = DDSAnalyticsCf[-ddsObs,]
dim(ddsTest) ## [1] 218 36Navmodel1= naiveBayes(ddsTrain[,-c(1,3,23,10,11,17)],ddsTrain$Attrition)
#Navmodel1= naiveBayes(ddsTrain[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)],ddsTrain$Attrition)
table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition)##
## No Yes
## No 172 7
## Yes 17 22#table(predict(Navmodel1,ddsTest[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)]),ddsTest$Attrition)
CM1= caret::confusionMatrix(table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition))
#CM1= confusionMatrix(table(predict(Navmodel1,ddsTest[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)]),ddsTest$Attrition))
CM1## Confusion Matrix and Statistics
##
##
## No Yes
## No 172 7
## Yes 17 22
##
## Accuracy : 0.8899
## 95% CI : (0.8406, 0.9282)
## No Information Rate : 0.867
## P-Value [Acc > NIR] : 0.18590
##
## Kappa : 0.5835
##
## Mcnemar's Test P-Value : 0.06619
##
## Sensitivity : 0.9101
## Specificity : 0.7586
## Pos Pred Value : 0.9609
## Neg Pred Value : 0.5641
## Prevalence : 0.8670
## Detection Rate : 0.7890
## Detection Prevalence : 0.8211
## Balanced Accuracy : 0.8343
##
## 'Positive' Class : No
## # Prediction
navPred <- predict(Navmodel1,DDSAnalyticsNoAttrition)
library(modelr)
predictionDataFrame <- add_predictions(DDSAnalyticsNoAttrition,Navmodel1,var="navPred",type = NULL)
navClassification <- predictionDataFrame[,c(1,36)]
write.csv(navClassification,"NbAttritionClassification.csv")
# Donut chart Nab Classification on DDSAnalyticsNoAttrition data set
nbClassification <- navClassification%>%group_by(navPred)%>%
dplyr::summarize(count=n(),
NavPredPercent = n()/nrow(navClassification))
nbClassificationDonut <- ggplot(data = nbClassification,aes(x=3,y=NavPredPercent,fill=navPred))+
geom_col(color="white")+
scale_fill_manual(values = c("#1E90FF", "#DC143C"))+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(NavPredPercent*100),"%")),
position = position_stack(vjust = 0.5),size=I(4.5),color="white")+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("naiveBayes Classification of Attrition")+
xlim(0.1,3.5)
nbClassificationDonut + labs(fill="Attrition") # change the legend title to Attrition from default title
# 200 iteration on Training / Test split Percentage to calculate accuracy
iterations = 200
masterAcc = matrix(nrow=iterations)
splitPerc = 0.75 #Training / Test split Percentage
for(j in 1:iterations){
ddsObs = sample(seq(1,dim(DDSAnalyticsCf)[1]),round(splitPerc*dim(DDSAnalyticsCf)[1]))
ddsTrain = DDSAnalyticsCf[ddsObs,]
ddsTest = DDSAnalyticsCf[-ddsObs,]
Navmodel1= naiveBayes(ddsTrain[,-c(1,3,23,10,11,17)],ddsTrain$Attrition,laplace = 1)
table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition)
CM1= caret::confusionMatrix(table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition))
masterAcc[j] = CM1$overall[1]
}
MeanAcc = colMeans(masterAcc)
MeanAcc## [1] 0.832844#logistic regression
#ddsPredictionData <- read.csv(file.choose())
ddsPredictionData<- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv",sep = ",",header = TRUE)
DDSAnalyticsLogistic <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)
DDSAnalyticsLogistic$OverTime = as.factor(DDSAnalyticsLogistic$OverTime)
set.seed(7)
ddsObslogis = sample(seq(1,dim(DDSAnalyticsLogistic)[1]),round(.75*dim(DDSAnalyticsLogistic)[1]),replace = FALSE)
ddsLogisTrain = DDSAnalyticsLogistic[ddsObslogis,]
dim(ddsLogisTrain)## [1] 652 36ddsLogisTest = DDSAnalyticsLogistic[-ddsObslogis,]
dim(ddsLogisTest) ## [1] 218 36logisModClf <- glm(Attrition ~ JobInvolvement+JobLevel+JobRole+JobSatisfaction+MaritalStatus+MonthlyIncome+MonthlyRate+
OverTime+StockOptionLevel+YearsAtCompany+YearsSinceLastPromotion+
YearsWithCurrManager+YearsInCurrentRole,
data = ddsLogisTrain, family=binomial(link="logit"))
summary(logisModClf)##
## Call:
## glm(formula = Attrition ~ JobInvolvement + JobLevel + JobRole +
## JobSatisfaction + MaritalStatus + MonthlyIncome + MonthlyRate +
## OverTime + StockOptionLevel + YearsAtCompany + YearsSinceLastPromotion +
## YearsWithCurrManager + YearsInCurrentRole, family = binomial(link = "logit"),
## data = ddsLogisTrain)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.92289 -0.55313 -0.27780 -0.09874 2.97668
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.441e-01 1.109e+00 0.310 0.756246
## JobInvolvement -7.887e-01 1.789e-01 -4.409 1.04e-05 ***
## JobLevel -3.760e-01 4.482e-01 -0.839 0.401495
## JobRoleHuman Resources 1.101e+00 8.326e-01 1.323 0.185906
## JobRoleLaboratory Technician 1.010e+00 6.758e-01 1.494 0.135213
## JobRoleManager 1.932e-01 1.062e+00 0.182 0.855658
## JobRoleManufacturing Director -1.236e+00 9.205e-01 -1.343 0.179254
## JobRoleResearch Director -1.360e+00 1.402e+00 -0.970 0.332129
## JobRoleResearch Scientist 3.898e-01 6.750e-01 0.577 0.563617
## JobRoleSales Executive 8.252e-01 5.868e-01 1.406 0.159653
## JobRoleSales Representative 1.499e+00 7.438e-01 2.016 0.043838 *
## JobSatisfaction -5.026e-01 1.185e-01 -4.242 2.22e-05 ***
## MaritalStatusMarried 1.379e+00 4.423e-01 3.118 0.001822 **
## MaritalStatusSingle 1.876e+00 5.375e-01 3.490 0.000483 ***
## MonthlyIncome 4.877e-05 1.187e-04 0.411 0.681311
## MonthlyRate -1.439e-05 1.810e-05 -0.795 0.426539
## OverTimeYes 1.797e+00 2.677e-01 6.713 1.91e-11 ***
## StockOptionLevel -1.044e-01 2.012e-01 -0.519 0.603812
## YearsAtCompany 1.131e-02 4.633e-02 0.244 0.807129
## YearsSinceLastPromotion 2.335e-01 5.887e-02 3.965 7.33e-05 ***
## YearsWithCurrManager -1.428e-01 6.184e-02 -2.309 0.020924 *
## YearsInCurrentRole -1.570e-01 6.332e-02 -2.479 0.013176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 594.98 on 651 degrees of freedom
## Residual deviance: 416.65 on 630 degrees of freedom
## AIC: 460.65
##
## Number of Fisher Scoring iterations: 6varImp(logisModClf) # displays all the important variables ## Overall
## JobInvolvement 4.4092415
## JobLevel 0.8389543
## JobRoleHuman Resources 1.3227870
## JobRoleLaboratory Technician 1.4938581
## JobRoleManager 0.1819037
## JobRoleManufacturing Director 1.3430570
## JobRoleResearch Director 0.9698350
## JobRoleResearch Scientist 0.5774769
## JobRoleSales Executive 1.4062395
## JobRoleSales Representative 2.0156343
## JobSatisfaction 4.2418899
## MaritalStatusMarried 3.1178152
## MaritalStatusSingle 3.4900623
## MonthlyIncome 0.4106743
## MonthlyRate 0.7951278
## OverTimeYes 6.7125276
## StockOptionLevel 0.5189261
## YearsAtCompany 0.2441314
## YearsSinceLastPromotion 3.9653132
## YearsWithCurrManager 2.3093618
## YearsInCurrentRole 2.4789736pred = predict(logisModClf, newdata=ddsLogisTest,type = "response")
table(ddsLogisTest$Attrition,pred > 0.5)##
## FALSE TRUE
## No 181 8
## Yes 13 16ddsPredictionData$OverTime=as.factor(ddsPredictionData$OverTime)
predLogistic <- predict(logisModClf,newdata = ddsPredictionData,type="response")
# Accuracy = 90.3%
# Sensitivity = 93.3%
# Specificity = 66.666%#https://www.shrm.org/hr-today/trends-and-forecasting/special-reports-and-expert-views/Documents/Retaining-Talent.pdf
# Turnover Analysis
# How Many Are Leaving?
# Who Is Leaving?
# Costs and Benefits?
#What causes turnover?
# satisfactry pay, good working condition and development opportunites.
# low job satisfaction
# poor performance
# more attractive than his or her current role
# work environment
# what make employee stay?
# positive work environment,
# promotional opportunities, status
# in the community,
lotisticCF <- glm(Attrition ~ Age+EnvironmentSatisfaction+JobInvolvement+JobSatisfaction+MaritalStatus+
RelationshipSatisfaction+TrainingTimesLastYear+WorkLifeBalance+YearsSinceLastPromotion+
JobRole+ MonthlyIncome+MonthlyRate+PerformanceRating+StandardHours+StockOptionLevel,
data = DDSAnalyticsCf, family = "binomial")
summary(lotisticCF)##
## Call:
## glm(formula = Attrition ~ Age + EnvironmentSatisfaction + JobInvolvement +
## JobSatisfaction + MaritalStatus + RelationshipSatisfaction +
## TrainingTimesLastYear + WorkLifeBalance + YearsSinceLastPromotion +
## JobRole + MonthlyIncome + MonthlyRate + PerformanceRating +
## StandardHours + StockOptionLevel, family = "binomial", data = DDSAnalyticsCf)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9929 -0.5836 -0.3547 -0.1711 2.8553
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.839e+00 1.440e+00 1.971 0.048678 *
## Age -2.821e-02 1.375e-02 -2.051 0.040288 *
## EnvironmentSatisfaction -1.808e-01 9.462e-02 -1.911 0.056022 .
## JobInvolvement -7.043e-01 1.423e-01 -4.949 7.47e-07 ***
## JobSatisfaction -3.869e-01 9.386e-02 -4.122 3.76e-05 ***
## MaritalStatusMarried 9.284e-01 3.698e-01 2.510 0.012060 *
## MaritalStatusSingle 1.570e+00 4.555e-01 3.447 0.000568 ***
## RelationshipSatisfaction -1.107e-01 9.248e-02 -1.197 0.231314
## TrainingTimesLastYear -2.597e-01 8.883e-02 -2.924 0.003457 **
## WorkLifeBalance -4.047e-01 1.434e-01 -2.823 0.004754 **
## YearsSinceLastPromotion 7.122e-02 3.624e-02 1.965 0.049378 *
## JobRoleHuman Resources 9.603e-01 6.963e-01 1.379 0.167854
## JobRoleLaboratory Technician 6.490e-01 5.167e-01 1.256 0.209127
## JobRoleManager -1.399e-01 9.133e-01 -0.153 0.878246
## JobRoleManufacturing Director -1.590e+00 8.296e-01 -1.916 0.055324 .
## JobRoleResearch Director -1.298e+00 1.204e+00 -1.078 0.281082
## JobRoleResearch Scientist 5.475e-01 5.146e-01 1.064 0.287317
## JobRoleSales Executive 4.580e-01 4.532e-01 1.011 0.312170
## JobRoleSales Representative 1.798e+00 5.747e-01 3.129 0.001755 **
## MonthlyIncome -2.395e-05 6.408e-05 -0.374 0.708549
## MonthlyRate -9.877e-06 1.488e-05 -0.664 0.506750
## PerformanceRating 1.868e-01 2.857e-01 0.654 0.513240
## StandardHours NA NA NA NA
## StockOptionLevel -9.890e-03 1.823e-01 -0.054 0.956730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 767.67 on 869 degrees of freedom
## Residual deviance: 608.81 on 847 degrees of freedom
## AIC: 654.81
##
## Number of Fisher Scoring iterations: 6Conclusion:
In nutshell, company is always going to have attrition, some are voluntary and some involuntary. The most important thing we are focus on this project is to find out a way to retain employee as much as possible. Voluntary attrition such as retirement, personal health, relocation etc. can be very costly to the company. On the other hand, involuntary attrition such as layoffs or reductions in force and terminating poorly performing employees can sometime be beneficial as it could create an opportunity to hire high performance and motivated employee that can better much a job role. While some of the factors that is analyzed in this project are not so easy to control attrition, there are some factors such as over time, stock option and job involvement that I believe can be controlled to retain the talented employees. Reducing overtime whenever its possible or spreading hours equally among several employee could be a solution to elevate the motivation and the performance of employee. As we all like to get appreciated and get involve in company and get noticed often, I am certain that focusing on improving the job involvement could be an excellent step to take to keep attrition in balance.